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Speech Disorders Classification in Phonetic Exams with MFCC and DTW 7

Speech Disorders Classification in Phonetic Exams with MFCC ...

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7th IEEE international conference on Collaboration and Internet computing (CIC)

作者： Liu, Jueting Speights, Marisha Bailey, Dallin Li, Sicheng Zhou, Huanyi Luan, Yaoxuan Xie, Tianshi Seals, Cheryl Auburn Univ CSSE Auburn AL 36849 USA Northwestern Univ CSD Evanston IL USA Auburn Univ CMDS Auburn AL USA Auburn Univ ECE Auburn AL USA

ISBN: (纸本)9781665416252

Recognizing disordered speech is a challenge to Automatic Speech Recognition (ASR) systems. this research focuses on classifying disordered speech vs. non-disordered speech through signal processing coupled with machine learning techniques. We have found little evidence of ASR that correctly classifies disordered vs. ordered speech at the level of expertbased classification. this research supports the Automated Phonetic Transcription - Grading Tool (APTgt). APTgt is an online E-Learning system that supports Communications Disorders (CMDS) faculty during linguistic courses and provides reinforcement activities for phonetic transcription with the potential to improve the quality of students' learning efficacy and teachers' pedagogical experience. In addition, APTgt generates interactive practice sessions and exams, automatic grading, and exam analysis. this paper will focus on the classification module to classify disordered speech and non-disordered speech supporting APTgt. We utilize Mel-frequency cepstral coefficients (MFCCs) and dynamic time warping (DTW) to preprocess the audio files and calculate the similarity, and the Support Vector Machine (SVM) algorithm for classification and regression.

关键词： E-Learning Phonetic Transcription international Phonetic Alphabet MFCC Dynamic Time Warping Speech Classification Support Vector Machine

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Unsupervised Embedding-based Metric for MT Evaluation with Improved Human Correlation 7

Unsupervised Embedding-based Metric for MT Evaluation with I...

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7th conference on Machine Translation, WMT 2022

作者： Mukherjee, Ananya Shrivastava, Manish Machine Translation - Natural Language Processing Lab Language Technologies Research Centre International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781959429296

In this paper, we describe our submission to the WMT22 metrics shared task. Our metric focuses on computing contextual and syntactic equivalences along with lexical, morphological and semantic similarity. the intent is to capture fluency and context of the MT outputs along with their adequacy. Fluency is captured using syntactic similarity and context is captured using sentence similarity leveraging sentence embeddings. the final sentence translation score is the weighted combination of three similarity scores: a) Syntactic Similarity b) Lexical, Morphological and Semantic Similarity and c) Contextual Similarity. this paper outlines two improved versions of MEE i.e., MEE2 and MEE4. Additionally, we perform our experiments on language pairs of en-de, en-ru and zh-en from WMT17-19 testset and further report the correlation with human assessments. Our submission will be made available at https://***/AnanyaCoder/WMT22Submission. © 2022 Association for Computational Linguistics.

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th international conference on Future Networks and Distributed systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. the present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. this study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. the proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. the findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. the proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660049

the proceedings contain 29 papers. the special focus in this conference is on Intelligent Information and Database systems. the topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency Image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological Data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for Visual Communication;Towards an UAV Visual Navigation System Based on Map Processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset;estimation of Distribution algorithms with Overlapped Subpopulations;energy-Efficient Edge Query Processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge computing through Co-design for Enhanced Data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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7th international conference on Actual problems of engineering mechanics, APEM 2020

7th International Conference on Actual problems of engineeri...

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7th international conference on Actual problems of engineering mechanics, APEM 2020

ISBN: (纸本)9783035718096

the proceedings contain 38 papers. the special focus in this conference is on Actual problems of engineering mechanics. the topics include: Experimental investigations of concrete on slag portland cement as a coating material for agricultural aerodromes;decorative concrete with the addition of highly dispersed organogenic calcite (Chalk) in the landscape architecture of the modern city;frost resistance of decorative composites of different composition;fractal model of the influence of expanded clay concrete macrostructure on its strength;determination of transition stages of structure formation by experimental data;modeling and optimization of technology and physics-mechanical properties of composite materials;laboratory studies of the heat-insulating properties of the panels that made of recycled rubber;the effect of silicate fillers on adhesion and adhesion strength properties of water-based coatings;the influence of the structure on the destruction mechanism of polymer-cement plaster coating;study of pavements of concrete paving blocks with ribbed underside surface;environmental problems of phosphogypsum utilization;corrosion resistance of polyester powder coatings using fillers of various chemical nature;on the question of the application of basalt microfiber for reinforcement of thermal insulation aac;modern systems of heat insulation buildings;ensuring strength of fine grained concrete with mixed cement binders;research of conditions of removal of fire protection from building construction;crack resistance and width of crack opening of beams with hybrid reinforcement using bfrp and metal armature;the study of the processes of structure formation in the system"portland cement-carbon nanotubes-surfactant" from the point of view of physicochemical mechanics of dispersed systems.

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IoT Network Based Analysis of Variations in Particulate Matter due to COVID-19 Lockdown 7

IoT Network Based Analysis of Variations in Particulate Matt...

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7th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2021

作者： Deb, Souradeep Reddy, C. Rajashekar Chaudhari, Sachin Vemuri, Kavita Rajan, K.S. International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781665428491

During the COVID-19 pandemic, India's complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad's lockdown, the capital of Telangana State. For this, two datasets are employed. the first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIIth in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. © 2021 IEEE.

关键词： COVID-19

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on Intelligent Information and Database systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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Research on PID Quadcopter UAV Control System Based on Expert System 24

Research on PID Quadcopter UAV Control System Based on Exper...

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proceedings of the 7th international conference on Information Technologies and Electrical Engineering

作者： Yiheng Xu International Engineering College Shenyang Aerospace University Shenyang Liaoning China

ISBN: (纸本)9798400707087

Quadcopter drones have many advantages and are being used more and more in various fields. this requires a stronger performance of the UAV. this paper adds an expert system algorithm on the basis of the traditional PID controller. Firstly, the kinematic modeling of the quadrotor UAV is undertaken to improve its control performance, the basic framework of the traditional PID controller is used first, and then the expert algorithm is added, and the controller parameters are adjusted through the analysis of the deviation to achieve a more accurate control effect. In this paper, the control simulation experiments of pitch angle, roll angle and yaw angle were carried out for both the traditional conventional PID controller and the optimised PID controller with additional expert algorithms, and it was shown by comparison that the expert PID controller takes less time to stabilise, has a smaller error and reduces the number of oscillations. It seems that the quadcopter UAV with expert PID control system has better control effect and stability.

关键词： Control System

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Bangladesh Crime Reports Analysis and Prediction 7

Bangladesh Crime Reports Analysis and Prediction

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7th international conference on Software Engineering and Computer systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Pavel Rahman, Md Ifranul Hoque, A. K. M Faysal Ahmed, Md. Iftekhirul, Iftekhirul Alam, Ashraful Hossain, Nahid United International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665414074

the systematic method of detecting crime, evaluating crime patterns, and anticipating crime trends is known as crime analysis and prediction. Crime is inherently unexpected and causes societal disruption. As Bangladesh's population grows, so does the prevalence of crime, which is wreaking havoc on our society in various ways. As a result, analyzing crime data has become crucial for a better understanding of future crime patterns. Machine learning and data mining techniques can be quite useful in predicting future crime trends and patterns in this situation. Various machine learning algorithms are utilized in this study to predict future crime patterns in Bangladesh. the crime statistics are gathered from the Bangladesh Police website to analyse and predict dacoity, robbery, murder, women and child repression, kidnapping, burglary, theft, and other crimes in Bangladesh's various regions. Another dataset from ACLED has been used to predict different kinds of events such as battles, explosions, protests, riots, strategic developments, violence against civilians with geolocations of the events. this research might assist Bangladesh police and law enforcement authorities to predict, prevent, and solve future crimes. the performance and success rate of the project are highly satisfactory. All resources of the project can be found at https://***/297yykmu © 2021 IEEE.

关键词： Forecasting

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Question Classification Framework for Helpdesk Ticketing Support System using Machine Learning 7

Question Classification Framework for Helpdesk Ticketing Sup...

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7th international conference on Research and Innovation in Information systems, ICRIIS 2021

作者： Harun, Noor Aklima Huspi, Sharin Hazlin Iahad, Noorminshah A. School of Computing Universiti Teknologi Malaysia Faculty of Engineering Johor Malaysia Azman Hashim International Business School Universiti Teknologi Malaysia Johor Malaysia

ISBN: (纸本)9781665408059

One of the elements that contribute to the nonuniformity of the question data in Helpdesk Ticketing Support (HTS) System is the diversity of services and users. Most questions that were asked in the HTS are in various forms and sentence styles but usually offer the same meaning. Various state-of-the-art machine-learning approaches have recently been used to automate the question classification process. Question classification, according to the researchers, is important to solve problems like helpdesk tickets being forwarded to the wrong resolver group and causing the ticket transfer process to take effect, and to associate a help desk ticket with its correct service from the start, reducing ticket resolution time, saving human resources, and improving user satisfaction. the key findings in the exploration results revealed that in HTS, tickets with a high number of transfer transactions take longer to complete than tickets with no transfer transaction. thus, this research aims to develop an automated question classification model for the HTS and proposes to apply the supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). the domain will use a readily available dataset from IT Unit. It is expected that this study will have a significant impact on the productivity of technical and system owners in dealing with the increasing number of comments, feedbacks, and complaints presented by end-users. this paper will present related works and research frameworks for automated question classification for HTS. © 2021 IEEE.

关键词： Support vector machines

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