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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4731-4740 订阅

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Comparative Analysis of Deep learning and Machine learning models for Network Intrusion Detection 14

Comparative Analysis of Deep Learning and Machine Learning m...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Dharaneish, V.C. Nithin Kumar, S. Hari Varsha, V. Senthil Kumar, T. Gireesh Kumar, T. Vajipayajula, Sulakshan Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Ibm India Pvt Ltd Ibm Security Bengaluru India

ISBN: (纸本)9798350335095

The increasing prevalence of security breaches and malicious software attacks is a major concern in the digital landscape, sparking continued interest in malware detection. Malware attacks have a significant impact on computer users, networks, businesses, organizations, and governments. Despite the development of multiple intrusion detection systems aimed at protecting data and resources from attacks, the frequent emergence of new threats and attacks poses a challenge for these systems to detect and prevent them from penetrating the network. One such attack is Advanced Persistent Threats (APTs) which can cause significant damage to computer network and organizations. To handle these attacks, the study has developed an APT detection system that uses various Machine learning (ML) and Deep learning (DL) based classifiers, which can more effectively extract data features from huge amounts of complex data and understand patterns to detect anomalies and potential threats. This study compares and evaluate their performance and efficiency on NSL-KDD dataset. By evaluating using various evaluation metrics, it was found that Extreme Gradient Boosting (XGBoost) is the most effective model among all models, followed by Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). © 2023 IEEE.

关键词： Network security

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Comparing Classical distance Measures and Word Embeddings for Automatic Short Answer Grading 9

Comparing Classical Distance Measures and Word Embeddings fo...

引用

9th international conference on Communication and Information Processing, ICCIP 2023

作者： Ripmiatin, Endang Purnamasari, Prima Dewi Ratna, Anak Agung Putri Department of Electrical Engineering Faculty of Engineering University of Indonesia Depok Indonesia

ISBN: (纸本)9798400708909

In the educational process, students' answers to essay questions are one of the cognitive methods to measure students' understanding of a topic being studied. But checking essay answers is certainly much more difficult than multiple-choice answers. Apart from absorbing much energy and time, it may also be biased depending on the human rater's subjectivity. To overcome this, researchers have already started to develop Automatic Short Answer Grading (ASAG) by exploring the field of natural language processing (NLP). However ASAG research specifically for Indonesian is still limited. This research aims to find the right method to improve the accuracy of the ASAG system in Indonesia focusing on the computer science domain, with a deep learning approach. We started our research by combining the feature extraction method Bag of Words and Term Frequency-Inverse Document Frequency (TF-IDF) with linear regression, Support Vector Regression, and Random Forest Regression. And then employing BERT and FastText. Both experiments produced similar performance, with an F1-score on average of 0, 72, which is categorized as low similarity. This opens opportunities for further research in word embeddings, especially the transformer method that becomes state-of-the-art. © 2023 Copyright held by the owner/author(s)

关键词： Grading

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A Survey of the Development of Image Captioning Techniques 3

A Survey of the Development of Image Captioning Techniques

引用

3rd international conference on computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Zhang, Fujun Zhao, Yinqiu National Languages Information Technology Boulder Northwest Minzu University Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Gansu Lanzhou730000 China

ISBN: (纸本)9781510671720

Image description technology is an important research direction in the field of deep learning. It is a task that uses computer vision techniques and natural language processing techniques to generate textual descriptions of the image features extracted from the corresponding images into high-level semantic information, i.e. to enable computers to learn the ability to "read pictures and talk". This paper collates several representative research methods that have emerged successively in the continuous development of image description. The popular template- and retrieval-based image description methods at the beginning of the research, and later, as deep learning flourishes, deep learning-based image description techniques have become mainstream, starting from end-to-end encoder-decoder, subsequently, the model began to be refined using the attention mechanism, and nowadays, new techniques based on Transformer technology and generative adversarial networks have greatly improved the accuracy of description. © 2023 SPIE.

关键词： computer vision

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IOT Driven Predictive Maintenance Using Machine learning Algorithms 6

IOT Driven Predictive Maintenance Using Machine Learning Alg...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rai, Anjani Kumar Pokhariya, Hemant Singh Tiwari, Kripanshu Divya Vani, V. Kumar, Devesh Kumar, Arjun Rana, Ajay Institute of Engineering & Technology GLA University Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Lloyd Law College Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The modern Web of Things (IIoT) alludes to the use of Web of Things (IoT) innovation underway that empowers the use of machine information created by different sensors and uses different investigation on it to get adroit information. The data typically accompany a date and time when they are caught by the gadgets. fundamental part for predictive demonstrating. The development of the Web of Things (IoT), which gives continuous information from sensors and associated gadgets, has totally changed the modern scene. Predictive support has turned into a progressive strategy for working on the efficiency and reliability of fundamental machinery and framework here. To empower IoT-driven predictive upkeep plans, machine learning algorithms assume a basic part, which is succinctly summed up in this theoretical. To anticipate hardware disappointments and identify upkeep needs before they bring about costly breakdowns, IoT-driven predictive support utilizes the consistent stream of information from sensors and gadgets. With their ability to break down huge measures of information and recognize complex examples, machine learning algorithms have become significant to this venture. © 2023 IEEE.

关键词： Algorithms IOT-Driven Machine learning Predictive

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Reward-Driven Clustered Federated learning for DRL Based UAV-RIS Networks 8

Reward-Driven Clustered Federated Learning for DRL Based UAV...

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8th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Wong, Yi Jie Tham, Mau-Luen Kwan, Ban-Hoe Iqbal, Amjad Lee, Ying Loong Chang, Yoong Choon Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang Malaysia Universiti Tunku Abdul Rahman Department of Mechatronics and Biomedical Engineering Kajang Malaysia Carleton University Department of System and Computer Engineering Canada

ISBN: (纸本)9798350315516

Deep reinforcement learning (DRL) has empowered numerous reconfigurable intelligent surface (RIS) aided millimeter-wave unmanned aerial vehicle (UAV) communications. However, most existing works often employ single-Task DRL, that could only handle one specific environment (or task). This hurdles the large-scale adoption of DRL-based UAV-RIS networks, since the environment has infinite variations. In this paper, we aim to solve diverse UAV-RIS tasks with a minimum number of DRLs required. Specifically, we proposed a reward-driven clustering technique to autonomously cluster all 50 simulated tasks into 10 clusters. For each cluster, we train a multi-Task DRL using Federated learning to handle all tasks in the cluster. Simulation shows that given 50 unique tasks, the proposed method could reduce the total agents required from 50 to 10, which is a 5-fold reduction. © 2023 IEEE.

关键词： Reinforcement learning

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IoT Architecture and Security Mecanisms for an Energy Management System in a Smart Microgrid 20

IoT Architecture and Security Mecanisms for an Energy Manage...

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20th international conference on Electrical engineering, Computing Science and Automatic Control, CCE 2023

作者： Campos-Sanchez, R. Hernandez-Martinez, L. Feregrino-Uribe, C. Inaoe Coordination of Electronics Puebla Mexico Inaoe Computer Science Coordination Puebla Mexico

ISBN: (纸本)9798350306767

In this work, we propose a system architecture that allows establishing decentralized control in a direct current (DC) smart microgrid (SMG), using an IoT 2.0 as a frame of reference for the communication between the elements of the digital ecosystem. This system aims to integrate control systems based on machine learning methods running on remote devices, taking advantage of smart systems applied in energy management for an efficient and secure operation of the DC SMG. © 2023 IEEE.

关键词： Internet of things

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A Review on Machine learning Models for Breathing Pattern Analysis of Soldiers

A Review on Machine Learning Models for Breathing Pattern An...

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2023 international conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Kaleeswari, P. Ramalakshmi, R. Thiyagarajan, Arunprasath Arunachalam, Muthukumar Ramaraj, Kottaimalai Thanga Raj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Krishnankoil Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Tamilnadu India Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil Tamilnadu India

ISBN: (纸本)9798350393378

Since 2001, the U.S. military has sent 2.7 million people to support missions in Afghanistan and Asia. The experience of land-based employees is increased by exposure to additional inhalational exposures and particulate matter from a variety of sources. For the purpose of preventing significant loss to the nation and to the individual military soldier, post-traumatic stress disorder (PTSD) must be identified. Breathing pattern analysis is a key method for detecting PTSD, and various studies have used machine learning techniques for this purpose. This survey examines multiple ML models to determine the military soldiers' breathing patterns in distinct works. This overview discusses several ML models and strategies used over the past few decades for conducting extensive research. Military personnel' breathing patterns are analyzed using a variety of datasets, statistical factors, and methodologies. The effectiveness of various algorithms is compared using qualitative as well as quantitative approaches. The potential future study areas with major challenges are discussed to reach a conclusion. © 2023 IEEE.

关键词： Personnel

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Pulmonary Nodule Detection in Computed Tomography Images Using Deep learning 1

Pulmonary Nodule Detection in Computed Tomography Images Usi...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Longkumer, Akumlong Walling, Supongmen Kalita, Shobhanjana Icfai University Department of Computer Science Dimapur India National Institute of Technology Department of Computer Science and Engineering Dimapur India Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350348057

the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is essential. For many years, the science of machine vision and medical diagnostics has used convolution neural networks. Early diagnosis can significantly lower the patient's mortality, and computer-aided diagnosis has emerged as a vital tool for medical professionals. computer-aided diagnosis depends on the ability to identify pulmonary nodules in computed tomography images. In the Computed Tomography scans, it is difficult to find the lump inside the lungs. This project uses U-Net to implement automated lung nodule identification. Furthermore, the data sets used for pulmonary nodule recognition from CT scan images are from the freely accessible LIDC/IDRI database. © 2023 IEEE.

关键词： computer aided diagnosis

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Human Posture Identification and Recognition Using Deep learning Techniques 11th

Human Posture Identification and Recognition Using Deep Lear...

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11th international conference on Intelligent Computing and Applications, ICRTC 2023

作者： Grover, Anuj Arora, Deepak Grover, Anant Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India

ISBN: (纸本)9789819717231

Human Pose Estimation is the task of estimating the human joints in a 2D or 3D coordinate plane from an input RGB image. The 2D HPE are of two types: top-down and bottom-up. The 3D Human Pose Estimation gives better understanding of the relative position of the joints of the person and is helpful to understand the positions of close joints. The evaluation metrics and the architectures for 2D HPE are discussed in brief in the paper. The paper focuses on 3D Human Pose Estimation because it provides more information about the depth of the joint and gives better spatial understanding of the joint’s position. The evaluation metrics and various 3D HPE architectures: Simple Baseline, Lifting from the Deep, Camera distance Aware approach, and BlazePose. A custom in-the-wild dataset was built and was used for evaluating the 3D Human Pose Estimation architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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Research on Power Optimization of Massive Machine Type of Communication Based on 5G Big Connection 4

Research on Power Optimization of Massive Machine Type of Co...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Zheng, Guoquan Liu, Yan Zhu, Enguo Zhang, Hailong Wang, Chaoliang Institute of Electrical Engineering Chinese Academy of Sciences Beijing China State Grid Zhejiang Electric Power Corporation Hangzhou China

ISBN: (纸本)9798350343113

In this paper, a resource allocation algorithm based on capacity maximization for massive Machine Type of Communication (mMTC) is studied. Taking LTE single cell as the background, considering the coexistence of LTE cellular user devices and mMTC devices, a two-layer network model is established in mMTC scenario, and mMTC gateway is introduced to analyze the transmit power control strategy of mMTC gateway (mMTCG) in mMTC scenario. On this basis, the bandwidth and power resource allocation problem in mMTC scenario is studied by using deep reinforcement learning algorithm to maximize the system capacity. The simulation results show that the proposed algorithm can improve the capacity of the system, and the power control is more consistent with the characteristics of mMTC scenario. © 2023 IEEE.

关键词： Reinforcement learning

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