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

共 40711 条记录，以下是4431-4440 订阅

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Hybrid Action Space D2D Resource Allocation Algorithm Based On Multi-Agent Reinforcement learning 3

Hybrid Action Space D2D Resource Allocation Algorithm Based ...

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3rd international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Guo, Jianding Chen, Jianying School of Computer Science and Engineering Southwest Minzu University Chengdu China

ISBN: (纸本)9798350372052

D2D (Device-to-Device) communication is one of the key technologies in 5G network. However, the resource allocation problem of D2D communication has become a bottleneck that affects network performance. This paper designs a multi-agent hybrid resource allocation algorithm (MHRA) for the channel and power allocation problem of D2D communication links while reusing uplink resources from the base station. The algorithm can handle both discrete channel allocation and continuous power allocation, and maximize network throughput while satisfying quality of service requirements. Each D2D link is modeled as an agent, and distributed reinforcement learning is performed based on the hybrid action space by sharing global state and immediate reward information. The paradigm of centralized training and distributed execution is used to improve algorithm performance. Simulation results show that the cumulative reward obtained by MHRA is better than that of the centralized DQN allocation algorithm under small-scale D2D networking, and MHRA is capable of solving larger scale D2D resource allocation problems. Compared with the distributed DQN algorithm, MHRA can significantly improve the cumulative reward, and has better stability and convergence speed. © 2023 IEEE.

关键词： Reinforcement learning

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An Efficient Way to Predict Stock Price using LSTM-RNN Algorithm 7

An Efficient Way to Predict Stock Price using LSTM-RNN Algor...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Polepally, Vijay Kumar Jakka, Neha Reddy Vishnukanth, Pendly Raj, Rachakatla Sushanth Anish, Gudavelli Kakatiya Institute of Technology and Sciences Department of Computer Science and Engineering Telangana Warangal India

ISBN: (纸本)9798350397253

Stock price prediction is a challenging and important task in finance. Machine learning algorithms have shown promise in predicting stock prices, with the ability to analyze large datasets and identify complex patterns. This study presents a comprehensive study of machine learning algorithms for stock price prediction. The proposed approach uses a variety of machine learning algorithms, including decision tree, random forest, support vector regression, and artificial neural networks. The models are trained on historical stock price data and evaluated using real-world data from various companies. This study calculates the RMSE, Error rate and the closing price of the stock for that day. © 2023 IEEE.

关键词： Forecasting

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Spaced Retrieval Practice Improves engineering Student Performance in Physics 53

Spaced Retrieval Practice Improves Engineering Student Perfo...

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53rd IEEE ASEE Frontiers in Education international conference, FIE 2023

作者： Bego, Campbell R. Tran, Alvin Ralston, Patricia A. S. Crockett, Cenetria Chastain, Raymond J. Lyle, Keith B. J. B. Speed School of Engineering University of Louisville Department of Engineering Fundamentals United States J. B. Speed School of Engineering University of Louisville Department of Computer Science and Computer Engineering United States College of Arts & Sciences University of Louisville Department of Anthropology United States College of Arts & Sciences University of Louisville Department of Physics & Astronomy United States Transfr Inc. Learning Intelligence New YorkNY United States

ISBN: (纸本)9798350336429

Spaced retrieval practice is an evidence-based learning technique that has the potential to improve student learning and memory in undergraduate courses. However, studies in STEM courses are limited, and results from available studies are inconsistent [1]. This paper presents such a study in an introductory physics course for engineering students, answering the following research question: Does spaced retrieval practice improve engineering student learning in physics? Students answered quiz questions that were administered in massed and spaced conditions throughout the semester, and then took a criterial test at the end of the semester. Results showed that spacing significantly improved student performance, with a mean gain of 3.4% in the spaced condition over the massed condition. This result is particularly interesting because a null effect of spacing was previously obtained in a similar physics course taken by non-engineering students [2]. Potential implications are discussed. © 2023 IEEE.

关键词： engineering education physics spaced retrieval practice STEM education

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Deep learning Based Vehicle Target Detection Algorithm Research 3

Deep Learning Based Vehicle Target Detection Algorithm Resea...

引用

3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Lv, Tianbao Zang, Jingfeng Changchun University of Science and Technology Department of Communications Engineering Changchun China Changchun University of Science and Technology Department of Electronic Information Engineering Changchun China

ISBN: (纸本)9798350316674

Vehicle detection is an indispensable and key part in autonomous driving technology. To address the problems of low recognition accuracy, poor real-time performance, lack of robustness of target detection algorithms in the realm of autonomous driving at this stage, This research paper suggests an enhanced YOLOv7 network for the study of vehicle recognition in the process of autonomous driving. The algorithm embeds a CBAM attention module in the backbone network of YOLOv7 to augment the capacity for extracting characteristics of the algorithm. The initial IOU-LOSS is substituted with EIOU-LOSS, and the loss component related to the aspect ratio is split into the disparity between the estimated width and height and the minimum outer frame width and height, respectively, to accelerate the rate of convergence. And YOLOv5s and YOLOv7 are introduced for comparison experiments. The empirical findings demonstrate that the improved YOLOv7-CBAM network exhibits a notable enhancement in vehicle target detection and outperforms the YOLOv5s and YOLOv7 algorithms. © 2023 IEEE.

关键词： CBAM attention module EIOU-LOSS Vehicle detection YOLOv7

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Research on IPFS resource retrieval system based on inverted index 4

Research on IPFS resource retrieval system based on inverted...

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4th international conference on Machine learning and computer Application, ICMLCA 2023

作者： Shi, Lin Lu, Jun College of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798400709449

In recent years, the spread of epidemics has prompted many industries to explore online services. The Inter Planetary File System (IPFS), as a peer-to-peer network-based protocol, this paper proposes a system architecture that adopts the idea of functional separation. Specifically, the retrieval nodes and data nodes are designed in separate clusters, and an inverted index is used in the retrieval cluster to improve the query speed of the index table, thereby optimizing the query process. © 2023 ACM.

关键词： Peer to peer networks

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A Review: Smart Farming using Internet of Things and Machine learning 10

A Review: Smart Farming using Internet of Things and Machine...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Singh, Prabjot Raj, Gaurav Kumar, Abhishek School of Engineering and Technology Sharda University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9789380544472

Internet of Things (IoT) is the new era for every new technology. The technology is basically used for every task in every industry for the purpose of the automation. IoT perform interaction of one machine with other to work in a proper way and provide with the suitable results. Many of the urban areas problems are resolved using these technologies with the combination of Machine learning and IoT. But now the use of these technology for the rural area people are advancing them and their businesses. Like-wise as IoT is used for Smart Homing, Smart Security, Smart Car, it is also be used for the purpose of the helping the farmers in the agriculture sector. The main study of this paper is to compare the different models used in smart farming and understanding the working of those model. This is a comparative study of the pre-given ways of helping the farmers with technologies in agriculture sector. Also, a theoretical model to increase the dataset and classifying the unhealthy fruit/vegetables is suggested. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Research on Abnormal Network Traffic Detection Method based on Machine learning 3

Research on Abnormal Network Traffic Detection Method based ...

引用

3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Tan, Xiaobo Jia, Yumei College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

Aiming at the problem of high false detection rate and low detection rate of abnormal network traffic detection methods due to uneven data distribution of current network traffic datasets, a network abnormal traffic detection method based on CNN-GRU is proposed. The method firstly performs local feature learning on the data according to the local learning ability unique to the convolutional neural network (CNN), and replaces the fully connected layer with a global average pooling layer to reduce the feature dimension and parameters;secondly, it uses a gated recurrent unit (GRU) learns the time series features of the data to improve the nonlinear representation ability of the method;finally, for the imbalance between the data, the cross entropy loss function is optimized, and the penalty for abnormal class sample detection and normal class sample detection is passed. The adjustment of the weight increases the attention of the model to the abnormal samples, thereby improving the accuracy of the model detection. The simulation results show that the method can effectively improve the classification and detection performance. © 2023 SPIE.

关键词： Convolutional neural networks

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The Solution of Multipath Fading Problem at Receiver in Underwater Acoustic Channel using Frequency Spectrum

The Solution of Multipath Fading Problem at Receiver in Unde...

引用

2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Rahman, Md. Mostafizur Akash, Rifat Alam Islam, Md. Hasibul East West University Department of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Bangladesh

ISBN: (纸本)9798350316001

The aim of the paper is to mitigate the effects of multipath fading at the receiver in underwater acoustic communication channel. Our approach leverages the frequency spectrum analysis of the composite received signal to identify the most reliable information-bearing frequency. By examining the magnitudes of frequencies present in the received signal, which may consist of multiple signals with varying degrees of orthogonality, we develop a technique to select a specific frequency as the true bit. This selection process is based on comparing the magnitudes of the frequencies within a given time interval. By choosing the frequency with either the highest or the lowest magnitude, we provide a solution that offers a probability of 1/2 for receiving accurate information. © 2023 IEEE.

关键词： Fast Fourier transforms

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Detection of Cyberbullying Using Modified Dense Framework 10

Detection of Cyberbullying Using Modified Dense Framework

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Mittal, Ruchi Singh, Vikram Mittal, Amit Singh, S Vikram Diwvedi, Shashi Prakash Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaudhary Devilal University Department of Computer Science and Engineering Sirsa India Chitkara Business School Chitkara University Punjab India Amity School of Engineering and Technology Amity University Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350382471

Cyberbullying has emerged as a significant societal concern, with the proliferation of digital communication platforms. Detecting and mitigating cyberbullying is crucial to ensure online safety and promote positive digital interactions. This study proposes an innovative approach for the detection of cyberbullying using a Modified Dense Framework (MDF). The Modified Dense Framework (MDF) introduced in this research is a deep learning architecture specifically tailored for cyberbullying detection. Unlike traditional methods, MDF combines the power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in a novel way, enhancing the model's ability to capture intricate patterns within textual and multimedia content. The framework is trained on diverse datasets encompassing text, images, and videos, enabling it to effectively identify various forms of cyberbullying across multiple online platforms. The study demonstrates the effectiveness of MDF through rigorous experimentation and evaluation. Comparative analyses with existing state-of-the-art models highlight the superiority of MDF in terms of accuracy, precision, recall, and F1-score. Moreover, MDF exhibits remarkable resilience against adversarial attacks, ensuring robust cyberbullying detection even in challenging scenarios. © 2023 IEEE.

关键词： Cyberbullying Deep learning Detection Modified Dense Framework Robust Detection

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Multiclass Predictor and Diagnoses of Breast Cancer Using Machine learning 12

Multiclass Predictor and Diagnoses of Breast Cancer Using Ma...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Chauhan, Nupa Ram Sengar, Alok Singh Shukla, Ratnesh Kumar Kumar, Saurabh Teerthanker Mahaveer University Computer Science & Engineering UP Moradabad India Noida International University Computer Science & Engineering UP Greater Noida India Shambhunath Institute of Engg. & Technology Computer Science & Engineering UP Prayagraj India

ISBN: (纸本)9798350369861

Metastatic Breast Cancer is the most prevalent and life-threatening diseases worldwide, affecting millions of women and men each year. Among all the most important factors that significantly influence the diagnosis and treatment an early stages are vital for reducing the fatality rate in the fight against it. Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. There are numbers of techniques as thermography, ultra-sound, mammography and magnetic resonance imaging (MRI) for the detection of cancer tumor. By combining these with machine learning techniques and automated diagnostic techniques, the potential for achieving highly accurate diagnoses with minimal false outcome. In this paper a novel intelligent integrated diagnosis system is proposed using classification models including Decision Tree Classifier, Naive Bayes Multinomial classifier, Support Vector Machines, K-Nearest Neighbor, XGBoost and Random forest using features selected at different threshold levels. The integration of machine learning techniques with clinical and histopathological data offers a powerful approach to analyzing complex patterns and relationships, thereby enhancing the ability to identify patients at high risk of metastasis in a timely manner. The performance and results evaluation shown an accuracy that varies for the models amongst five proposed approaches gives 94% to 99%, with a precision value of 95% to 98%, sensitivity from 96% to 99 %, and with specificity from 93% to 97%. This work will always support to the patient and doctors to take help of this model to predict the complexity of the breast cancer and its type to reduce the risk of this life threatening diseases. © 2023 IEEE.

关键词： Support vector machines

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