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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是201-210 订阅

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From Titles to Genres: An Exploration of machine learning Techniques in Movie Classification 5th

From Titles to Genres: An Exploration of Machine Learning Te...

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5th international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2023

作者： Zhang, Zhaohuan College of Information Science and Technology/College of Cyber Security Jinan University Guangzhou China

ISBN: (纸本)9789819751839

the application of machine learning models to identify the genre of movies based on their titles and overviews is explored in this research paper. the "Movie Title" dataset from Kaggle is the source of the data for this investigation. the study examines the data, carries out the necessary cleaning and manipulation, creates a word cloud to visualize frequently occurring terms, resamples the data for a better distribution of genres, and employs two models for genre prediction: BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency-Inverse Document Frequency). the performance of both models is evaluated using accuracy and/or loss metrics, and conclusions are drawn based on the findings. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distribution transformers

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Analysis and Identification of Evil Twin Attack through Data Science Techniques Using AWID3 Dataset 6

Analysis and Identification of Evil Twin Attack through Data...

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6th international conference on machine learning and machine Intelligence, MLMI 2023

作者： Da Silva, Leandro Marcos Andreghetti, Vinicius Moraes Romero, Roseli Aparecida Francelin Branco, Kalinka Regina Lucas Jaquie Castelo Institute of Mathematics and Computer Sciences University of São Paulo Brazil Department of Electrical and Computer Engineering University of São Paulo Brazil

ISBN: (纸本)9798400709456

Wi-Fi is a widely used technology worldwide but can also be a source of insecurity. One of the most common forms of attack is the evil twin, where an attacker creates a fake Wi-Fi network with the same name as a legitimate network. machine learning algorithms are a promising solution to identify this type of attack. Existing works use databases known as Aegean Wi-Fi Intrusion Datasets (AWID) and detect different types of attacks. this paper focuses on the evil twin attack on AWID3 based on analysis and detection, exploring data science techniques, encompassing the steps of pre-processing, data analysis, data balancing, training and validation, and model testing. the results indicate that the optimized LightGBM reached a False Positive Rate (FPR) of 0.00602 in the test with all columns of the dataset, in addition to an FPR of 0.00898 using a column set, promising for an Intrusion Detection System (IDS). © 2023 ACM.

关键词： machine learning

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machine learning-Based Text Categorization with Bag of Words 6th

Machine Learning-Based Text Categorization with Bag of Words

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Singh, Uday Kumar Prabhu Shankar, B. Chinnaiyan, R. Jain, Neeraj Alliance University Bengaluru India

ISBN: (纸本)9789819728381

Text categorization, a fundamental task in natural language processing (NLP), plays a pivotal role in organizing and managing the ever-expanding volume of textual data across various domains. this research explores the machine learning applications techniques, with a specific emphasis on the Bag-of-Words (BOW) model, to automate the categorization of text documents. the BOW model is a straightforward yet effective representation method that transforms text data into numerical vectors, disregarding the word order and focusing solely on word frequency. BOW approach is to deal with the representation of text that can be used to any sort of the organization of text. this method is based on BOW concept, which measures the material available on Wikipedia, Gmail, Kaggle (https://***/datasets), and other sites. the suggested approach is employed to create a Vector Space Representation, subsequently employed to educate a Support Vector machine categorizer. the purpose is to arrange and gather document records from openly accessible datasets through social networking. the textual outcomes exhibit the contrast between the unprocessed data and the purified data exhibited on the word cloud." © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Research on the Memory processing Rules of Children in Tang Poetry Reading Using Internal Feature Extraction and Deep Neural Network Method 6

Research on the Memory Processing Rules of Children in Tang ...

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6th IEEE international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Zhao, Wang Ke, Zhao Chinese Academy of Sciences State Key Laboratory of Brain and Cognitive Science Institute of Psychology Beijing China University of Chinese Academy of Sciences Department of Psychology Beijing China

ISBN: (纸本)9798350323535

there are differences in children's cognitive abilities towards Chinese Tang poetry. Several Tang poems are easier for children to learn, while others are difficult. thus, it is necessary to understand what characteristics of Tang poetry make children find difficult to learn. this problem was modeled as a binary classification problem in machine learning. Firstly, a Tang poetry recitation test was conducted on children, and the average score of all children on each poem was calculated. After data processing, a measure of the difficulty of Tang poetry was determined as the dependent variable for the study. Tang poetry has many characteristics, such as theme type, publication period, and style depending on whether it rhymes, the number of words, number of sentences, sentence length, and whether there are rare characters. these features were selected as independent variables. We constructed two classification models based on the classification and regression tree (CART) and deep neural network methods. the F1 scores of the methods were 80.3 and 84.8%. Although the accuracy of the CART model was slightly lower, it still showed strong interpretability for the characteristics of Tang poetry that were difficult for children to learn. the results can assist in practical teaching. © 2023 IEEE.

关键词： Decision trees

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machine learning Algorithms for Diabetes Diagnosis Prediction 24

Machine Learning Algorithms for Diabetes Diagnosis Predictio...

引用

6th international conference on Image, Video and signal processing, IVSP 2024

作者： Zambrana, Amanda Fanek, Loreen Carrera, Pablo Salar Ali, Md Liakat Narasareddygari, Mourya Reddy Dept. Of Computer Science And Physic Rider University NJ United States

ISBN: (纸本)9798400716829

Diabetes is one of the most common health conditions in the United States, affecting more than 37 million U.S. adults. Despite how many people are affected by this disease, a permanent cure does not currently exist. therefore, it is very important for patients to get the proper diagnosis and treatment. If diabetes is not diagnosed and properly treated to control blood sugar levels, serious health problems can occur. though undiagnosed cases are very dangerous, still 1 in 5 U.S. adults with diabetes do not know that they have it. therefore, a method for early detection or prediction of the disease is imperative for healthcare workers to provide timely distribution of treatment. Presently, hospitals collect large volumes of patient data with various tests and provide appropriate treatment based on diagnoses. With big data analytics, hidden patterns can be found in the data that help to predict diagnosis outcomes. Such predictions can help lower the rate of undiagnosed patients. In our paper, we propose a prediction model for classification of diabetes based on various features of patient data. We developed and applied six machine learning algorithms on two diabetes data sets to predict diagnoses. We found that the best algorithms for prediction with our two data sets were Ridge Classifier with an accuracy of 83.12%, along with Random Forest and Decision Tree classifiers, both achieving accuracies of 95%. © 2024 ACM.

关键词： Forecasting

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Enhanced Classification Models for Heart Disease Prediction in Precision Medicine 6

Enhanced Classification Models for Heart Disease Prediction ...

引用

6th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Boyko, Nataliya Dosiak, Iryna Lviv Polytech Natl Univ Stepana Bandera St 12 UA-79000 Lvov Ukraine

this investigation endeavors to scrutinize and enhance machine learning (ML) algorithms tailored for medical diagnostics, specifically in the prognostication of cardiac pathologies. the focal point of this study encompasses medical indicators such as electrocardiograms (ECGs), diagnostic outcomes, and symptomatic manifestations, examining their pivotal role in the efficacious identification of cardiac ailments. the research delves into mathematical data mining models and their classification synergies, constituting the core subject of inquiry. the scholarly contribution of this work lies in the refinement of ML methodologies, thereby bolstering the precision in cardiac disease diagnostics. From a practical standpoint, the research aspires to forge a sophisticated classification schema, poised to mitigate the incidence of diagnostic inaccuracies. the discourse underscores the pertinence of ML algorithm deployment in the medical domain, particularly for cardiac diagnostics. A meticulous review and critique of extant scientific literature and analogous studies have been conducted, alongside an evaluation of mathematical and informational frameworks. the study delineates fundamental classifiers and their collective ensembles, with a pronounced focus on discerning the associative patterns between cardiac conditions and various indicators. the research illuminates the impact of demographic factors, such as age and smoking habits, as well as comorbidities, laboratory metrics, symptomatic expressions, test outcomes, and ECG readings on cardiac diagnostic processes. It also contrasts the symptomatic disparities between genders in both cardiac patients and healthy cohorts. An array of classification methodologies and their ensembles have been employed to facilitate the automated diagnosis of cardiac diseases. Extensive trials utilizing diverse ML classification techniques have been executed. the investigation extends to feature selection algorithms and data transformation tec

关键词： technology processing analysis machine learning algorithms classification diagnosis heart diseases

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Transforming the Prediction of Heart Disease: An Empirical Analysis of machine learning Classifiers 6

Transforming the Prediction of Heart Disease: An Empirical A...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Soni, Tanishq Gupta, Deepali Uppal, Mudita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350324204

the most prevalent diseases impacting the lives of individuals are those connected to the human body's most critical organ, the heart. It is crucial to emphasize that heart disease problems are not a new phenomenon and should not be sought after or welcomed as a trend, even if heart-related issues have received more attention and debate in recent years as a consequence of increased understanding and research. By leveraging their data processing and analytical skills, computers, Artificial Intelligence (AI) and the internet have revolutionized the identification and prediction of cardiac disease. the suggested model is evaluated and compared to current models that have been shown to be more accurate and effective, such as K-nearest Neighbor, Decision Tree, Logistic Regression, Random Forest and many other machines learning and deep learning Logistic Regression, Random Forest, K- nearest Neighbor and Decision Tree. After comparing and testing many models using various criteria, Random Forest revealed that the suggested model had the greatest accuracy 82.72%. © 2023 IEEE.

关键词： Diseases

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Robust Sentiment Classification Based on the Backdoor Adjustment 6

Robust Sentiment Classification Based on the Backdoor Adjust...

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6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Dai, Lulu Han, Mingyue Shanghai University of Finance and Economics China Fujian Agriculture and Forestry University China

ISBN: (纸本)9798400709241

Deep NLP models are correlation-based learning, which has a critical limitation of over-fitting over spurious features and shows poor generalization capability in the out-of-distribution (OOD) setting. Existing methods encourage the model to exploit causal features and exclude spurious correlations through Counterfactually Augmented Data (CAD) and feature regularization. However, those methods still face challenges due to the low quality of counterfactual generation and the high cost of annotation. this paper proposes an improved method for OOD generalization motivated by causal inference tools. Specifically, taking the topic of the text as the confounder of the input and the label, the model fits the causal correlation between the representations and the label through the backdoor adjustment to alleviate the exploitation of the spurious correlations. the proposed method is evaluated on the counterfactual adversarial test set (movie review text) and the challenging test set with synonym perturbation (financial news text) provided in the previous work. the experimental results show that this method improves the OOD generalization capability of the sentiment classification models in these two attacks while preserving the predictive ability, especially in the case of long text. © 2023 ACM.

关键词： Text processing

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Facing the 6G: an AI-enhance quantum-secured fronthaul architecture

Facing the 6G: an AI-enhance quantum-secured fronthaul archi...

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5th international conference on signal processing and machine learning (CONF spml 2025)

作者： Jiayi Wang Nanjing University of Aeronautics and Astronautics No. 29 Jiangjun Avenue Jiangning District Nanjing Jiangsu Province People's Republic of China

ISBN: (数字)9781837243143

Amidst the development of communication technologies, the limitations of 5G communication systems, particularly in security and resource management, have become increasingly apparent. As the 6G era approaches, there are growing demands for integrated systems that ensure robust security and optimize resource utilization. To address these challenges, this paper proposes a novel architecture that integrates classical communication channels and quantum key distribution (QKD) channels using wavelength-division multiplexing (WDM). this innovative approach allows classical and quantum channels to coexist within a single optical fiber, improving security and fiber utilization while reducing system complexity and cost. A key contribution of this work is introducing an AI-based prediction and management module, dynamically adjusting the quantum key generation rate according to real-time usage and forecasted demand. By leveraging machine learning, the module predicts quantum key consumption patterns, ensuring that the key generation rate aligns with the system’s security and performance requirements. this approach balances key generation and usage, minimizing resource waste and enhancing the system’s overall efficiency. the feasibility of the proposed architecture is validated through an analysis of spontaneous Raman scattering (SpRS), showing the architecture maintains high key generation rates and low quantum bit error rates (QBER) under optimal system conditions. Furthermore, an analysis reveals that the AI module meets the requirement of predicting key usage and managing the key generation rate. Lastly, the feasibility is further validated by an experiment. Overall, this study presents a comprehensive framework for integrating classical and quantum channels in future 6G networks, providing enhanced security, efficiency, and scalability.

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Graph Routes From Local and Global Entrances 23

Graph Routes From Local and Global Entrances

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6th international conference on Big Data Technologies, ICBDT 2023

作者： Cheng, Miao Wang, Limin School of Computer Science Guangxi Normal University Guangxi Guilin China Co. Ltd. Shanghai China

ISBN: (纸本)9798400707667

As an important structure of data handling, many categories of network data are normally organized as graphs, and graph routes represent the intrinsic connectivity of vertices, which can be obtained as the filtered results of graph signal processing. Till now, there has aroused broad interests on the valid solutions to graph representation learning, which can be obtained from optimized learning of graph routes. Based on the previous contributions of author, an improved approach to graph routes detection is proposed in this work, which conducts graph routes from local and global entrances simultaneously. As a consequence, the optimization can be enhanced and adaptable straightforward. In accordance with the experimental results, the proposed method is able to present optimistic results of graph routes detection while efficient performance can be reserved. © 2023 ACM.

关键词： signal processing

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