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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4721-4730 订阅

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Channel Estimation at THz Frequency Using cGAN for Massive MIMO System 26

Channel Estimation at THz Frequency Using cGAN for Massive M...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Tasnia, Reefah Ahmed, Samiha Zuairia Nazrul Islam, A.K.M. Chanda, Dibaloke Ul Kabir Chowdhury, Mohammad Jawad Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh Marquette University Department of Computer Science MilwaukeeWI53233 United States

ISBN: (纸本)9798350359015

In wireless communication, accurate channel estimation is crucial for receiver performance, but predicting the channel in massive MIMO systems is challenging. In this paper, a conditional generative adversarial network (cGAN) is used for accurate channel estimation in a massive MIMO system with one-bit analog-to-digital converters (ADCs). Compared to other deep learning models, cGAN produces high-quality outputs and enables conditional generating by training the generator and discriminator in an adversarial manner to minimize information loss. In this research, we have successfully applied this model to a wide variety of scenarios at 1.4 Terahertz (THz) frequency and achieved satisfactory performance. The simulation results show that in an outdoor 6G massive MIMO system at THz range frequency, an approximate 3 dB gain in estimation error is obtained than in indoor at GHz range frequency. © 2023 IEEE.

关键词： cGAN channel estimation deep learning massive MIMO THz frequency

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A Study on Cervical Cancer Prediction using Various Machine learning Approaches

A Study on Cervical Cancer Prediction using Various Machine ...

引用

2023 IEEE international conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Rawat, Priyanshu Bajaj, Madhvan Mehta, Shreshtha Sharma, Vikrant Vats, Satvik Graphie Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Biotech Engineering Dehradun India

ISBN: (纸本)9798350397208

Cervical cancer is a major cause of mortality for women, and early detection is crucial for successful treatment Recent studies have investigated the use of machine learning for early detection of cervical cancer, but challenges remain. This paper evaluates the performance of different machine learning algorithms, including logistic regression, bagging, random forest, and XG Boost, for predicting cervical cancer. The study analyzes challenges in working with cervical cancer data, such as dealing with imbalanced datasets and limited data availability. To address these challenges, the paper proposes an approach that combines the strengths of the different algorithms to develop a more accurate and reliable model for early detection of cervical cancer. To assess the effectiveness of the proposed approach, the study uses standard metrics, including accuracy, precision, recall, and F1 score. The findings indicate that the proposed approach outperforms the individual machine learning algorithms in terms of predictive accuracy and precision. The paper emphasizes the need for further research in this area and highlights the potential of machine learning to enhance the early detection of cervical cancer. By proposing a new approach that addresses the challenges faced by existing methods, the paper aims to contribute to efforts to improve cervical cancer detection and treatment. © 2023 IEEE.

关键词： Decision trees

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A High-Resolution Grassland Aboveground Biomass Estimation Method Based on SfM and Grassland Height Factor

A High-Resolution Grassland Aboveground Biomass Estimation M...

引用

2023 IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2023

作者： Cao, Mengdi Cao, Shanshan Sun, Wei Kong, Fantao Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China College of Computer and Information Engineering Xinjiang Agricultural University Urumqi China Institute of Special Animal and Plant Sciences Chinese Academy of Agricultural Sciences Changchun China

ISBN: (纸本)9798350309614

In this paper, a high-resolution grassland aboveground biomass estimation method was constructed based on the UAV SfM (Structure from Motion) technique and the grass layer height factor. Various vegetation index data of grassland were extracted from the images acquired by multispectral camera, grass layer height data were extracted by SfM technique, and these explanatory variables were combined with Random Forest, and Support Vector Regression Machine learning methods to construct a high-resolution aboveground biomass estimation model of rangeland grassland, respectively. The method introduces the grass layer height factor into the estimation model to improve the estimation accuracy, and the UAV SfM method can realize the rapid and accurate estimation of the aboveground biomass of the grassland in the pasture area, and obtain the high-resolution aboveground biomass of the grassland at a relatively low cost, and provide decision support for the herdsmen to drive the herds during the rotational area grazing. © 2023 IEEE.

关键词： grass layer height grassland biomass SfM

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Cardiovascular Disease Prediction Using Machine learning 6

Cardiovascular Disease Prediction Using Machine Learning

引用

6th international conference on Information Systems and computer Networks, ISCON 2023

作者： Divyasri, Parise Sreelakshmi, D. Sathvika, Pathuri Teja, Polimetla Charan, Tumati Vidya Prasad v Potluri Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada520007 India

ISBN: (纸本)9798350346961

The leading cause of death worldwide is heart disease. An effective hybrid classifier model is finally constructed to classify records and produce predictions or identifications based on significant input factors. The findings of this study lower healthcare costs and enable cardiologists to diagnose heart disease more reliably. In this context, an adaptive voting classifier is a type of ensemble learning method that combines the predictions of multiple classifiers to improve accuracy and robustness. This paper presents a heart disease prediction model based on a voting classifier, which combines the predictions of individual classifiers: a decision tree, support vector machine (SVM), k-nearest neighbors (KNN) classifier, Random Forest, and XGBoost. The models used in this study will also be helpful in situations when many patients show up daily. The application would use a few attributes about the patient's physical state and medical history. On evaluating the proposed adaptive voting-based feature selection for classification has attained an accuracy of 99.83%, and the model has outperformed compared to the other existing state-of-art models considered in the evaluation.. © 2023 IEEE.

关键词： Forecasting

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A Study of Deep learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

引用

international conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Electroencephalography (EEG) Based Prediction of learning and Responsiveness among Students 7

Electroencephalography (EEG) Based Prediction of Learning an...

引用

7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Agrawal, Alok Kumar Ahmed, Bilal Jakhar, Yogesh Kumar Bajaj, Karan Kumar, Girish Kumar, Mukesh Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India School of Computer Application Lovely Professional University India University of Engineering & Management Department of Computer Application Jaipur India

ISBN: (纸本)9798350340600

The constructivist method of teaching has been shown to have a major influence on the learning outcomes of students. In order to improve classroom instruction, it is essential to evaluate students' levels of involvement and attentiveness while they are being taught. Electroencephalography (EEG) can now be used to assess students' attentiveness in real time thanks to recent neuro-engineering breakthroughs and mobile brainwave sensors' availability. The purpose of this study is to provide an overview of EEG-based techniques with the intention of predicting constructivism and engagement among students of varying levels. The purpose of this study is to evaluate the efficacy of these approaches and to determine the extent to which they have the potential to enhance the design of future learning systems. This survey analyzes whether or not approaches based on EEG may be used to determine the level of constructivism and involvement displayed by pupils. The findings provide helpful insights that can be used to develop tailored teaching strategies and improve education's overall efficiency. However, some obstacles need to be overcome in order for EEG-based predictions to be accurate. Some of these obstacles include data quality and variability, individual differences, interpretability, generalization, and ethical considerations. © 2023 IEEE.

关键词： Forecasting

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Proceedings - 2023 IEEE international conference on Software Maintenance and Evolution, ICSME 2023

Proceedings - 2023 IEEE International Conference on Software...

引用

39th IEEE international conference on Software Maintenance and Evolution, ICSME 2023

ISBN: (纸本)9798350327830

The proceedings contain 71 papers. The topics discussed include: security and safety verification in IoT apps;summarize me: the future of issue thread interpretation;DeltaNN: assessing the impact of computational environment parameters on the performance of image recognition models;revisiting machine learning based test case prioritization for continuous integration;deploying deep reinforcement learning systems: a taxonomy of challenges;software bill of materials adoption: a mining study from GitHub;GPTCloneBench: a comprehensive benchmark of semantic clones and cross-language clones using GPT-3 model and SemanticCloneBench;you augment me: exploring ChatGPT-based data augmentation for semantic code search;and slicing shared-memory concurrent programs the threaded system dependence graph revisited.

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Forecasting Equity Prices using LSTM and BERT with Sentiment Analysis 6

Forecasting Equity Prices using LSTM and BERT with Sentiment...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Ayyappa, Yalanati Kumar, B. Vinay Priya, Sudhabatthula Padma Akhila, Siddiboena Reddy, Tamma Priya Vardhan Goush, Shaik Mohammad Faculty Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada520007 India Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada520007 India

ISBN: (纸本)9798350398496

In the recent past, the utilization deep learning techniques to estimate stock prices has drawn a lot of research interest. This research study examines the application of BERT (Bi-directional Encoder Representations from Transformers) and LSTM (Long Short-Term Memory) for stock price forecasting utilizing historical stock data and sentiment classification of twitter posts. The proposed model combines the power of LSTM for capturing the sequential patterns in stock prices and the contextual understanding ability of BERT in analyzing tweet sentiment. The results of the empirical evaluation showed that in terms of accuracy and robustness, the proposed model surpassed conventional time-series models and other models based on deep learning. This study provides a proof-of-concept that the combination of LSTM and BERT can be a promising approach for stock price prediction. Furthermore, the study highlights the potential for future research to explore other deep learning techniques and data sources to improve the accuracy and robustness of stock price prediction models. © 2023 IEEE.

关键词： Long short-term memory

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Nearest Neighbor Based Unsupervised Deep learning Image Recognition Method

Nearest Neighbor Based Unsupervised Deep Learning Image Reco...

引用

2023 IEEE international conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Solomon, Enoch Woubie, Abraham Emiru, Eyael Solomon Virginia State University Department of Computer Science RichmondVA United States Silo AI Helsinki Finland University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350393415

Although deep learning are commonly employed for image recognition, usually huge amount of labeled training data is required, which may not always be readily available. This leads to a noticeable performance disparity when compared to state-of-The-Art unsupervised face verification techniques. In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation. Notably, the autoencoder is trained to reconstruct neighboring face image vectors rather than the original input image vectors. These neighbor face image vectors are chosen through an unsupervised process based on the highest cosine scores with the training face image vectors. The proposed method achieves a relative improvement of 56% in terms of EER over the baseline system on Labeled Faces in the Wild (LFW) dataset. This has successfully narrowed down the performance gap between cosine and PLDA scoring systems. © 2023 IEEE.

关键词： Face recognition

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Segmenting Sonar Images with an Enhanced OTSU Algorithm 2

Segmenting Sonar Images with an Enhanced OTSU Algorithm

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2nd international conference on Image Processing, computer Vision and Machine learning, ICICML 2023

作者： Yang, Fan University of Jinan School of Electrical Engineering Jinan250022 China

ISBN: (纸本)9798350331417

Sonar image segmentation technique is crucial for underwater target tracking, among other things. Due to the undersea environment's influence, noise is easily absorbed, which leads to a poor tracking performance. In order to address the issues with the traditional one-dimensional and two-dimensional OTSU algorithms, which have a slow computation time and poor anti-noise performance, this paper proposes a fast OTSU segmentation algorithm based on wavelet boosting and analyses the algorithmic principles and implementation steps of one-dimensional OTSU algorithm, two-dimensional OTSU algorithm and its fast algorithm, fast algorithm with the variance as the eigenvalue, and maximum entropy algorithm. On the basis of this, the aforementioned algorithms are used to perform image segmentation on four photos. The segmentation findings demonstrate the method's quick processing speed and strong noise resistance, which have significant implications for sonar image segmentation applications. © 2023 IEEE.

关键词： image segmentation OTSU second generation wavelet transform sonar images

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