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共 729 条记录，以下是201-210 订阅

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Speech Language Identification Using CNN-BiLSTM with Attention Mechanism 3

Speech Language Identification Using CNN-BiLSTM with Attenti...

引用

3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Yilihamu, Dilizhati Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Aiming at the problems of low feature extraction ability and low recognition rate in language identification task, this paper proposes CNN-BiLSTM language identification method based on attention mechanism from the two aspects of feature and model. In the feature extraction stage, we compare the recognition effects of different speech features such as MFCC, LPCC and spectrogram, Experiments show that the recognition effect of spectrogram features is better in the actual environment. This method extracts the spectrogram of speech data, uses convolution network to obtain the visual features of spectrogram, and introduces attention mechanism, which can pay attention to the language related information in speech fragments and ignore non language information. This method is tested on two cross-channel data sets (AP19-olr) and (AP20-olr) of the Oriental Language Recognition competition. Compared with the language identification methods such as the baseline system x-vector, the equal error rate (EER%) on the two data sets is reduced by 3.38% and 4.66%, The results clearly show that the CNN-BiLSTM language identification method based on attention mechanism effectively improves the feature extraction ability in language identification task, and improves the performance of language identification in practical application environment. © 2022 IEEE.

关键词： Feature extraction

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Hybrid Combination of machine learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System 3rd

Hybrid Combination of Machine Learning Techniques for Diagno...

引用

3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Ganu, Likha Arun, Biri Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Yupia791110 India

ISBN: (纸本)9789811908392

Nowadays, liver impairment is one of the most complicated diseases in the world which is increasing very rapidly. Around 50 million adult population in the world are highly affected by cirrhosis liver impairment which is the 12th leading cause of death in 2020. In this paper, we survey and compare various machine learning techniques that help the clinical specialist to determine the risk of liver impairment and will aid the physician in the process of making decision. This paper will conduct a hybrid combination of machine learning techniques and was compared to other machine learning algorithms to determine the best performance. To this end, the study is carried out on the Indian liver patient dataset (ILPD). Firstly, data preprocessing is done to eliminate the irreverent feature and to identify the relevant feature using K-medoids clustering-based attribute weighting (k-MCAW), and for feature selection, PCA is integrated, and for classification, SVM is being carried out. The proposed hybrid method (k-MCAW + PCA + SVM) classifies the dataset into healthy or unhealthy classes. Then the performance is predicted by using the SVM classification technique in terms of accuracy, sensitivity analysis, F-measure, specificity, kappa statics value. The result shows that our purposed hybrid model is a prominent tool for valuable tool for the clinical decision support system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Analysis of the Relationship Between COVID-19 and Diet Based on machine learning Algorithm

Analysis of the Relationship Between COVID-19 and Diet Based...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Hao Jie Li Yan Liu Yong Gansu Engineering Research Center for Eco-Environmental Intelligent Networking School of Electrical Engineering Northwest Minzu University Lanzhou China

In this paper, the data set of food and nutrition intake from the Food and Agriculture Organization of the United Nations (FAO) and COVID-19 case counts from the World Health Organization (WHO) are used to analyze the relationship between diet and COVID-19 infections in different countries. Different machine learning algorithms, like regression, clustering, and classification, are used to process and analyze the data set. In addition, an improved random forest method for feature selection is proposed. This paper has studied the relationship between food intake and obesity or undernourished, the association between the amount of food consumed and the amount of fat, protein, and calories consumed through that food, and the impact on the confirmed, deaths and recovery rate of COVID-19. The purpose is trying to find some foods that can prevent or assist in the treatment of COVID-19, and achieve a non-pharmaceutical intervention for COVID-19 through a healthy diet.

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Area and Delay Efficient Adders for Inexact Computing Implementation

Area and Delay Efficient Adders for Inexact Computing Implem...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： K. Arunkumar Mallireddy Venkata Nithin Reddy Manamasi Bhavya Department of ECE Saveetha Engineering College Chennai Tamilnadu

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Recently, inexact computing has gained a lot of interest in modern computational systems since it can improve the use of resources particularly when the exact precision is not critical. Here, the proposed 24-bit Approximate Ripple Carry Adder (ARCA) features a hybrid design based on simplified logic operations that embed approximate computation for LSBs and exact computation for MSBs. This method significantly reduces the power consumption, footprint, and latency of traditional precision adders. Extensive simulations reveal that ARCA outperforms its competitors with regard to resource savings while preserving almost all the computational precision. The results clearly indicate that our approach reduces power consumption by 27%, delay by 29%, and area by 25% compared to traditional ripple carry adders. The above results demonstrate the effectiveness of ARCA in many resource-constrained applications, including embedded systems, digital signal processing, machine learning, and multimedia.

关键词： Embedded systems Power demand Simulation Multimedia systems machine learning Digital signal processing Delays Logic Adders Optimization

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Unified Parsing Script using machine learning

Unified Parsing Script using Machine Learning

引用

international Symposium on Artificial Intelligence and signal processing (AISP)

作者： G Sudha Sadasivam Kumar G Barath Visva R Tarun S Mukesh R Shashaank Preeti Muley Computer Science and Engineering PSG College of Technology Coimbatore India Ugam Solutions Coimbatore India

Gathering noiseless data is an important phase in fields such as machine learning and target advertising and is also one of the most tedious processes. The evolution of the Internet has made it the largest source of data, in every domain. Web scraping can be defined as the process of extracting and parsing information from unstructured web pages. Advancement in web scraping has provided us with tools such as BeautifulSoup, lxml, and Selenium, which precisely extract information from webpages based on the knowledge of the HTML document to be parsed. Nevertheless, one major inconvenience associated with such primitive web scrapers is that they are site-dependent. They have to be adapted for each new website. In this paper, we propose a machine learning based approach that searches for patterns in the semantics of the webpages of a single domain, capable of acting as a site-independent web scraper. The strategy proposed involves an algorithm that utilizes the results of three independent machine learning models to extract key information from web pages belonging to a specific domain. The experimental analysis of the simulation demonstrates that the solution proposed enhances the process of web scraping by enabling a single web scraper to extract data accurately from multiple websites, thus improving productivity.

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Comparative analysis of machine learning algorithms to detect fake news 3

Comparative analysis of machine learning algorithms to detec...

引用

3rd international conference on signal processing and Communication (ICPSC)

作者： Indarapu, Sai Rama Krishna Komalla, Jahnavi Inugala, Dheeraj Reddy Kota, Gowtham Reddy Sanam, Anjali KITS Warangal Dept CSE Warangal Andhra Pradesh India

ISBN: (纸本)9781665428644

Fake news has immense impact in our modern society. The widespread dissemination of false news has the potential to have highly damaging consequences for both individuals and society. As the readers come across many fake news when they come across a real news, they believe that it could be another fake news. The aim of this project is to perform a comparative analysis of three algorithms (Multinomial Naive Bayes, Passive Aggressive Classifier and Decision Tree Classifier) using Natural Language processing techniques to develop a solution that users can use to identify false or misleading information. As Passive Aggressive Classifier gave the best results, prediction is done using this classifier.

关键词： fake news naive bayes decision tree passive aggressive classifier count vectorizer tf-idf vectorizer confusion matrix

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Cloud Computing and Covid-19 3

Cloud Computing and Covid-19

引用

3rd international conference on signal processing and Communication (ICPSC)

作者： Singh, Raju Arizona State Univ Tempe AZ 85202 USA

ISBN: (纸本)9781665428644

At present the spread of Covid-19 (Coronavirus disease) has become a major threat to human health. Covid-19 is a deadly disease and it is the main reason behind many heath causalities all over the world as it's hard to detect and even harder to contain. Every nation has taken serious measures to control the spread of the disease. In the fight against the Coronavirus, the modern-day technologies play an extraordinary role. The technologies mitigate the effects of Coronavirus up to great extent. The technologies such as artificial intelligence (AI), Cloud computing, Internet of things (IoT), Sensor technologies, Robotics etc., contributes a lot to mitigate the effects. Out of these technologies, the Cloud computing and storage stands one step ahead in its contribution. The Cloud services are used to store, monitor, predict and analyze the huge set of patient's data. This translates the tedious task of data processing into relatively easy one and helps mankind.

关键词： Cloud Computing Covid-19 IoT Biosensors Telemonitoring machine learning Systems (MLS) Artificial Intelligence

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AI-Enabled Analysis of 3-D CT Scans for Diagnosis of COVID-19 & its Severity

AI-Enabled Analysis of 3-D CT Scans for Diagnosis of COVID-1...

引用

Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE international conference on

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece

This paper describes the 3rd COVID-19 Competition, taking place in the AI-enabled medical image analysis (AIMIA) Workshop of the 2023 IEEE international conference on Acoustics, Speech and signal processing (ICASSP 2023). The 3rd COVID-19 Competition is a continuation of the Competitions held at ECCV 2022 and ICCV 2021 conferences, and aims to tackle the challenges of whole slide image and CT/MRI/X-ray analysis/processing and to identify research opportunities in the context of Digital Pathology and Radiology/COVID19. The 3rd COVID-19 Competition consists of two Challenges targeting COVID19 detection and COVID19 severity detection. Both Challenges are based on an extended version of the database used in the 1st and 2nd COV19D Competitions, the COV19-CT-DB database, which includes chest CT scan series. A large part of the COV19-CT-DB database is annotated for COVID-19 detection and consists of 8,0003-D CT scans. About 1,0003-D CT scans of the database are also annotated with respect to four COVID-19 severity conditions. Both parts have been split in training, validation and test datasets. These are used for training and validation of machine learning models, as well as for evaluation. The paper further describes the baseline methods for the 3rd COVID-19 Competition, which are deep learning approaches, based on CNN-RNN networks. Their performance on detecting the existence and the severity of COVID-19 is reported.

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Optimized SVM based on improved whale algorithm for EEG signal pattern classification

Optimized SVM based on improved whale algorithm for EEG sign...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Junqiang Peng Lijun Liu Tiangong University Tianjin China

In order to improve the classification accuracy of moving image electroencephalogram ( EEG ) signals, aiming at the problem that the traditional support vector machine (SVM ) parameter value is not accurate, which leads to poor classification model, this study introduces Sobol sequence, reverse learning strategy and piecewise nonlinear time-varying factor. In order to improve the random layout strategy of boundary processing, Levy flight strategy improves the traditional whale algorithm, combines the improved whale algorithm with SVM, and compares the combined SVM classifier with other classifiers for EEG signal classification. The results show that the accuracy of the optimization algorithm is 2 ~ 4 percentage points.

关键词： Support vector machines machine learning algorithms Sociology Pattern classification Whales Brain modeling Electroencephalography

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Comparing the Predictive Accuracy of machine learning Algorithms for Neonatal Mortality Risk Classification 3rd

Comparing the Predictive Accuracy of Machine Learning Algori...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Sivarajan, A. Bala Aditya, A. Sivasankar, E. Department of CSE National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9789811908392

Neonatal mortality is a major concern in developing countries and is a crude indicator of the prevalence of poverty in a society. Neonatal risk analysis is an important decision-making tool for gauging progress and penetration of medical services in a specific region, especially in developing countries. This research compares the accuracy of the predictions of various machine learning algorithms and the impact of different sampling techniques, when attempting to classify neonatal mortality. We have conducted different rounds of experiments with six sampling techniques and seven machine learning algorithms on a neonatal death dataset. Further, groups of three base classifiers chosen among the seven machine learning algorithms were made into six different ensemble combinations and trained to improve the classification accuracy. The proposed diagnostic support system with an ensemble of random forest classifier, Gaussian NB, and AdaBoost with pipeline sampling achieved an accuracy and ROC AUC scores above 98% and 96%, respectively. This decision support tool will facilitate medical practitioner to identify infants at risk of dying before 28 days of life. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Developing countries

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