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Accuracy Optimization in Speech Pathology Diagnosis with data Preprocessing Techniques 1

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3rd international conference on Optimization, Learning algorithms and Applications (OL2A)

作者： Teixeira Fernandes, Joana Filipa Freitas, Diamantino Rui Teixeira, Joao Paulo Inst Politecn Braganca IPB Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300 Braganca Portugal Univ Porto FEUP Fac Engn P-4200465 Porto Portugal Inst Politecn Braganca Associate Lab Sustainabil & Technol SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Using acoustic analysis to classify and identify speech disorders non-invasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessing methodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalization models and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure.

关键词： Outliers Normalization Speech Pathologies Speech Features Machine Learning Vocal Acoustic Analysis

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Vision Transformer Based Hash Coding for Efficient Image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： Image coding

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Augmenting Ridership data with Social Media data to Analyse the Long-term Effect of COVID-19 on Public Transport 8

Augmenting Ridership Data with Social Media Data to Analyse ...

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8th international conference on models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Xu, Yanyan Krishnakumari, Panchamy Yorke-Smith, Neil Hoogendoorn, Serge Delft Univ Technol Fac Civil Engn & Geosci NL-2628 CN Delft Netherlands Delft Univ Technol Star Lab NL-2600 GA Delft Netherlands

ISBN: (纸本)9781665455305

COVID-19 significantly influenced travel behaviours and public attitudes towards public transport. Various studies have illustrated complicated factors related to long-term travel behaviour, indicating difficulty in understanding and predicting post-pandemic long-term travel behaviour via traditional methods. In these complex circumstances, it is valuable to take advantage of social media data to obtain real-time public opinions to understand dynamic travel behaviour changes from the passenger perspective. The present study provides a means - leveraging Twitter data and state-of-art Natural Language processing (NLP) technologies - to interpret the underlying associations among public attitude, COVID-19 trends and public travel behaviour. Concretely, New York City has been selected due to its dependence on public transit for daily commuting. More than 500K tweets have been collected from January 2019 to June 2022. Automated text mining, topic modelling, and sentiment analysis have been implemented in these contexts to identify dynamic public reactions. A consistently negative attitude to public transit is detected and five main topics, including derivative topics from COVID-19, are discovered within the COVID-19 duration. Policy makers and transit managers can use these topics to take onboard the public's concerns. The paper thus exemplifies how social media data and NLP technologies can support policy-making progress and can benefit other tasks in the transportation domain.

关键词： public transport travel behaviour social media topic modelling sentiment analysis COVID-19 natural language processing

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21th international conference on Intelligent data Engineering and Automated Learning, IDEAL 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on Intelligent data Engineering and Automated Learning, IDEAL 2020

ISBN: (纸本)9783030623647

The proceedings contain 34 papers. The special focus in this conference is on Intelligent data Engineering and Automated Learning. The topics include: Network Analysis for Fraud Detection in Portuguese Public Procurement;biased Language Detection in Court Decisions;a One-by-One Method for Community Detection in Attributed Networks;data Pre-processing and data Generation in the Student Flow Case Study;a Hybrid Approach to the Analysis of a Collection of Research Papers;Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images;time Series Clustering for Knowledge Discovery on Metal Additive Manufacturing;quaternion Neural Networks: State-of-the-Art and Research Challenges;a Solar Thermal System Temperature Prediction of a Smart Building for data Recovery and Security Purposes;a Fault Detection System for Power Cells During Capacity Confirmation Test Through a Global One-Class Classifier;a Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects;improving Performance of Recommendation System Architecture;automated Learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network;Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces;enhanced Credit Prediction Using Artificial data;deep Learning Based algorithms for Welding Edge Points Detection;detecting Performance Anomalies in the Multi-component Software a Collaborative Robot;prediction of Small-Wind Turbine Performance from Time Series Modelling Using Intelligent Techniques;review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual data;atmospheric Tomography Using Convolutional Neural Networks;workshop on Machine Learning in Smart Mobility;Driver Monitoring System Based on CNN models: An Approach for Attention Level Detection;preface.

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Design of Point Cloud data Structures for Efficient processing of Large-Scale Point Clouds

Design of Point Cloud Data Structures for Efficient Processi...

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international conference on Optical and Photonic Engineering (IcOPEN)

作者： Wang, Yixuan Li, Xudong Zhao, Fenglin Jin, Zhehui Tang, Yong Zhao, Huijie Beihang Univ Sch Instrumentat & Optoelect Engn 37 Xueyuan Rd Beijing 100191 Peoples R China Key Lab Precis Optomechatron Technol 37 Xueyuan Rd Beijing 100191 Peoples R China Beihang Univ Qingdao Res Inst 339 Songling Rd Qingdao 266000 Shandong Peoples R China

ISBN: (纸本)9781510674561;9781510674578

Existing three-dimensional scanning techniques enable the acquisition of dense point clouds representing the surface of the scanned object. However, the voluminous nature of unordered point cloud data leads to extended data processing times, necessitating the utilization of specific data structures for the management of large-scale point clouds. Addressing the performance degradation issue of prevalent point cloud data structures when dealing with a large quantity of points, this paper initiates a comparative analysis of common point cloud data structures, encompassing grid-based, quadtree, and k-d tree (k-dimensional tree) indexing methods. Through theoretical derivations, an examination of the time complexities of various data structures is undertaken. Building on this theoretical foundation, an empirical quantitative assessment of the real-world performance of distinct data structures is executed. Leveraging the insights gained from these analyses, this paper further capitalizes on the inherent shape characteristics of empirically acquired point cloud data to introduce a novel three-tier hybrid indexed point cloud data structure, accompanied by its corresponding algorithmic functionalities. This innovative structure amalgamates grid-based, quadtree, and k-d tree indexing strategies. Empirical findings demonstrate that, when applied to large-scale point clouds, the proposed three-tier hybrid indexed data structure exhibits enhanced indexing establishment speed and neighborhood search velocity compared to conventional algorithms. Thus, this work establishes a foundational data structure support for subsequent processing and application of large-scale point cloud data.

关键词： Large-scale point clouds point cloud data structures point cloud data processing 3D measurement

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Improving Mental Disorder Predictions using Feature-Based Machine Learning Techninques 23

Improving Mental Disorder Predictions using Feature-Based Ma...

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23rd IEEE international conference on data Mining (IEEE ICDM)

作者： Muetunda, Faustino Pais, Sebastiao Sabry, Soumaya Dias, Gael Pombo, Nuno Cordeiro, Joao Univ Beira Interior Dept Comp Sci Covilha Portugal Univ Beira Interior Covilha Portugal NOVA LINCS Covilha Portugal Univ Caen Normandie GREYC Caen France Univ Beira Interior Inst Telecomunicacoes Covilha Portugal INESC TEC Covilha Portugal

ISBN: (纸本)9798350381641

Maintaining good mental health is essential for physical well-being and is a significant global concern. Mental disorders are complex to diagnose due to similar and common symptoms for multiple mental illnesses. Intelligent systems can aid in accurately identifying mental illnesses, which is critical for analyzing and quickly recovering patients. In this work, we applied feature selection with a correlation matrix to enhance machine learning models' performance. Our goal was to predict mental health issues and extract insights from datasets. The data used for experiments contains three different datasets from a mental health assistance unit in Spain, depressive symptomatology in adolescents in Ghana and children with autism spectrum disorders in Italy. The metrics like PHQ-9, WEMWBS, SSS-8, GAD-7, C-SSRS, and CBCL were implemented to normalise the dataset. Evaluation using K-fold cross-validation of the selected optimal features was measured by applying Support Vector Machines(SVM), Logistic regression(Log R), K-Nearest Neighbors algorithm (KNN), Kernel SVM (KSVM), Naive Bayes (NB), Decision Trees, Random forest (RF), and two ensemble methods Boosting and Bagging classifiers. Five evaluation metrics, namely, Accuracy, F1-score, Precision, Recall, and AUC, showed the best performance with Boosting for depressive symptomatology in adolescents in Ghana with an overall Accuracy(0.81), F1-score (0.83), Precision (0.83), Recall (0.83), and AUC (0.95). Bagging for the prevalence of psychiatric problems in Spain with an overall Accuracy(0.94), F1-score (0.94), Precision (0.95), Recall (0.93), and AUC (0.94) and Bagging for autism spectrum disorder with an overall Accuracy(0.81), F1-score (0.83), Precision (0.83), Recall (0.83), and AUC (0.95). Mental health datasets are difficult to arrange and sometimes are limited in size, which is insufficient to train the machine learning models for predicting mental disorders. The training of ML algorithms using correlated features sim

关键词： Machine Learning data Mining Ensemble Methods Mental Health Mental Disorder and Classification algorithms.

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21th international conference on Intelligent data Engineering and Automated Learning, IDEAL 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on Intelligent data Engineering and Automated Learning, IDEAL 2020

ISBN: (纸本)9783030623616

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Time-Sensitive data processing Strategy for Enhancing the Performance of BFT Consensus Mechanism in IoT Edge Computing Environment 23

Time-Sensitive Data Processing Strategy for Enhancing the Pe...

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2nd international conference on algorithms, data Mining, and Information Technology, ADMIT 2023

作者： Qian, Cheng Tang, Wenzhong Wang, Yanyang School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798400707629

Applying the BFT consensus mechanism in the IoT edge computing environment effectively solves the problem of data consistency and trustworthiness. However, the existing BFT consensus mechanism needs to pay attention to the impact of the data processing flow on consensus performance, and the repetitive data processing operations lead to the waste of computing resources of consensus nodes. Therefore, in this paper, we analyze the data processing flow of the BFT consensus mechanism, propose a time-sensitive data processing strategy from the idea of reducing the number of consensus nodes involved in data processing, and select data processing nodes by taking data processing time as the optimization target to improve the resource utilization of consensus nodes while ensuring consensus reaching. Through simulation experiments, our proposed time-sensitive data processing strategy achieves the best latency and throughput performance in a simulated IoT edge computing environment, demonstrating that the time-sensitive data processing strategy can effectively improve the performance of the BFT consensus mechanism. © 2023 ACM.

关键词： Internet of things

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A survey of data-driven insights into air quality trends and prediction in India 4th

A survey of data-driven insights into air quality trends and...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Kabilan, K. Jebathangam, J. Department of Computer Science VISTAS Chennai India

ISBN: (纸本)9781032911571

Air pollution is a major health and environmental issue in increasingly urbanizing countries like India. This study examines ML and DL implementations on Indian Air Quality Index (AQI) datasets from 2015 to 2020. The research seeks to identify patterns, anticipate trends, and guide air pollution mitigation decisions. The study carefully analyses AQI parameters, explores data, and uses powerful ML and DL algorithms to forecast AQI and identify pollution hotspots. Technical features include missing value imputation, feature engineering, model selection criteria, hyper parameter tuning procedures, and model performance assessment measures. Key results show that ML and DL models can accurately forecast air quality, identify pollution sources, and capture temporal and geographical fluctuations. The article suggests integrating more data sources, exploring advanced deep learning methods, and developing dynamic air quality control systems. This study uses data-driven insights and multidisciplinary techniques to improve our knowledge of air quality dynamics in India and create the framework for proactive air pollution mitigation and public health policies. © 2025 the Author(s).

关键词： data quality

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HA-MARL: Heuristic and APF Assisted Multi-agent Reinforcement Learning for Wireless data Sharing in AUV Swarms 59

HA-MARL: Heuristic and APF Assisted Multi-agent Reinforcemen...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Li, Zonglin Du, Jun Jiang, Chunxiao Mi, Weishi Ren, Yong Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100083 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100083 Peoples R China

ISBN: (纸本)9781728190549

This paper focuses on the design of intelligent game strategy for multi-autonomous underwater vehicle (multi-AUV) underwater network system. The challenge lies in ensuring the coordination and stability between AUVs in complex underwater environments. To meet underwater data sharing requirements, we formulate an intelligent game strategy incorporating communication delays by formulating the problem as a partially observable Markov decision process (POMDP). Additionally, to address the issue of sparse rewards during exploration in multi-agent reinforcement learning (MARL) models and improve the coordination among AUVs, we propose a heuristic and artificial potential field (APF)-assisted multi-agent proximal policy optimization (HA-MAPPO) algorithm. Our proposed scheme addresses the issue of sparse rewards in MARL by using APF as path planner and subsequently utilizes heuristic algorithm for task scheduling to achieve optimal goal allocation. Simulation results demonstrate that our proposed HA-MAPPO algorithm outperforms current mainstream MARL algorithms regarding convergence speed while maximizing the winning rates.

关键词： Markov processes

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