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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是101-110 订阅

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EMG-based Gesture Recognition using Extreme learning machine

EMG-based Gesture Recognition using Extreme Learning Machine

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Tanishqa Tyagi Anukul Pandey Kaustubh Ranjan Singh Department of Electronics and Communication Engineering Delhi Technological University (formerly Delhi College of Engineering) Delhi India

Electromyography (EMG) signals are crucial data to track muscle activities making them a key point for design of prosthetic devices. In order to classify the finger movements in upper limb prothesis, a recognition method based on an extreme learning machine (ELM) is studied in this paper. The target to be recognized is constructed by grouping two kinds of hand gestures, individual finger movements, and combined finger movements. The recognition feature matrix is established by decomposing the windowed signal using wavelet transform and extracting conventional time-domain features from that. These features are then fed to three different classifiers for recognition of binary class. Currently, the best accuracy achieved using ELM is above 95%, outperforming Support Vector machine (SVM) and Fine Trees in this study

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Comparison of machine learning Methods for Classification and Diagnosis of Skin Disease

Comparison of Machine Learning Methods for Classification an...

引用

international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Kakarla Deepti S. Aruna Deepthi Department of ECE Vasavi College of Engineering Hyderabad INDIA

Skin disease is one of the most prevalent diseases around the world. To ease the complexity of conventional computational methods, deep learning techniques are adopted. By leveraging transfer learning and deep learning models, Convolution neural networks have been built, trained and evaluated. From the available data set of 10,082 using CycleGAN data augmentation a total of 39507 images are generated. One challenge faced during the implementation is mainly to improve the stability of CycleGAN during training phase. To analyze the performance of CycleGAN, three CNN models based on architecture of MobileNet, LeNet and modified LeNet are considered. A comparative analysis based on performance of three architectures is done and it is concluded that modified LeNet has outperformed with a higher accuracy of 95.03%.

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Perspective of signal processing-based on brain-computer interfaces using machine learning methods

Perspective of signal processing-based on brain-computer int...

引用

3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： He, Ruyue Department of Electronics and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781643684321

The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The 'Discussion' part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms. © 2023 IOS Press. All rights reserved.

关键词： Brain computer interface

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A machine learning Model to Classify Indian Taxi System in Tourism Industry

A Machine Learning Model to Classify Indian Taxi System in T...

引用

international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Krutibash Nayak Saroj Kumar Panigrahy School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

India is now becoming a tourism hotspot for tourist. To support the growing tourism industry, the taxi services play a major role and also it plays an important role in urban transportation. In view of the popularity of Taxi services, we have analyzed the sentiment of the taxi industry by taking the reviews of the customer on different taxi service providers. In this research, we addressed text sentiment analysis of taxi reviews, posted by customers on online review sites. All the reviews are based on Indian review sites only. We have compared many machine learning techniques with the dataset. To determine the sentiments of text reviews, machine learning techniques are used, which explore the feeling of a customer and also give the in-hand idea of the taxi services and its amenities. The study presents that among all the common machine learning techniques, Support Vector machine (SVM) performs better than other algorithms. Considering different evaluation parameters like Accuracy, F1Score, and Recall value, SVM gives the best result with 89%, 82%, and 86% respectively.

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Intelligent Software Bug Prediction: An Empirical Approach

Intelligent Software Bug Prediction: An Empirical Approach

引用

international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： MD. Ariyan Jahangir MD. Abtahi Tajwar Waythin Marma Md. Siyamul Islam Department of Computer Science American International Univeristy-Bangladesh Dhaka Bangladesh

Despite the fact that, much research has been conducted to improve accuracy in software bug prediction through different machine learning (ML) classifiers, not concentrated on the performance evaluation on the applicability of ML algorithms to detect software bugs. This inadequacy is focused on this paper. In this research, we conducted software bug prediction comparison on six different ML algorithms. Moreover, we adopted Code Based Metrics (CBM) to predict software defect through sequential neural network (DL) model and compared it with generic models. The performance of different models has been evaluated and compared based on PROMISE dataset provided by NASA. Results have shown that ML Techniques and DL Approaches have similar bug prediction capabilities where Decision Tree technique performing the worst and Support Vector machine gave the best results. Also, thoughtful feature selection provides noticeable difference compared to no feature selection during model construction.

关键词： Support vector machines machine learning algorithms Computer bugs Software algorithms signal processing algorithms Predictive models Prediction algorithms

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Classification of Phishing Websites using machine learning Models

Classification of Phishing Websites using Machine Learning M...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Santhosh Raminedi Trilok Nath Pandey Venkat Amith Woonna Sletzer Concy Mascarenhas Arjun Bharani Department of Computer Science Vellore Institute of Technology Chennai India

Phishing attacks have become increasingly and it is a serious threat to security of the user information. It targets unsuspecting individuals through deceptive websites that appear to be legitimate. These attacks can compromise sensitive information and also have serious consequences. machine learning has a potential solution to address the growing threat of phishing. By training algorithms to recognize the tell-tale signs of phishing websites, it may be possible to detect and prevent these attacks before they can cause harm. In our paper, we explored the features of detecting the phishing url with the use of ML algorithms such as SVM, Logistic Regression, Naive Bayes classifier, and also ensembling algorithm such as Random Forest Classifier. We examine the features that are commonly used in these algorithms and compare their effectiveness in accurately classifying websites as either Phishing or Legitimate

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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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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...

引用

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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Unified Parsing Script using machine learning

Unified Parsing Script using Machine Learning

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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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