检索结果-内蒙古大学图书馆

Delay-Constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways 19th

Delay-Constrained Multicast Throughput Maximization in MEC ...

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Junyi Wei, Zhenchun Yuan, Xiaohui Lyu, Zengwei Feng, Lin Han, Jianghong School of Computer Science and Information Technology Hefei University of Technology Hefei China Department of Computer Science and Engineering University of North Texus DentonTX United States

ISBN: (纸本)9783031545306

Multi-access Edge Computing presents a compelling solution for delivering seamless connectivity to computing services. In this study, we aim to optimize multicast throughput to ensure high-quality experiences for passengers engaged in inter-train interactions within dedicated MEC networks designed for high-speed railways. Considering the unique challenges associated with high-speed railways, we model multicast routing paths as group Steiner trees. Subsequently, we devise a rapid tree construction method by converting the root search into the Generalized Assignment Problem (GAP). This innovative approach skillfully balances accuracy and computational efficiency. We demonstrate that this problem can be effectively reduced to the bounded knapsack problem with setups. In addition, we recognize the presence of precedence constraints between tasks and their respective outcomes. Consequently, we introduce a new variant of the knapsack problem, which we refer to as the Precedence-constrained Bounded Knapsack Problem with Setups. Our approach, termed the GAP- and knapsack-based Group Steiner Tree (GKGST), offers a relative performance guarantee of 1/2. We evaluate the GKGST algorithm against three baseline algorithms, which are adapted and extended from existing literature. Simulation results indicate that our proposed algorithm exhibits considerable potential for enhanced performance. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Capsule Network Approach for Image Classification 4th

Capsule Network Approach for Image Classification

引用

4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Marjit Singh, Moirangthem Kumar Sarkar, Nayan Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Arunachal Pradesh Itanagar India

ISBN: (纸本)9789819994410

Numerous deep learning (DL) models based on convolutional neural network (CNN) have been developed for image classification. Though the CNN models attained outstanding performance, its architecture contains certain limitations such as loss of information in the pooling layer and no spatial relationship among the features of an object. The development of capsule network (CapsNet) has overcome the drawbacks of CNN. The CapsNet works on a set of capsules, and each capsule comprises a set of features. The CapsNet consists of a convolution layer, a primary capsule layer, image caps and a fully connected (FC) layer. In this paper, a variant of CapsNet approach is proposed by tuning the parameters of the original CapsNet and replacing ReLU with AReLU activation function in each FC layer. The classification performance of the proposed approach is evaluated on the Plant Village (PV) and Tomato datasets. 80% of the datasets were trained and 20% were tested. The proposed approach considering filter size of 5 × 5 in the feature extraction layer with AReLU activation function in each FC layer delivers 94.02% and 94.06% classification accuracies on PV and Tomato datasets respectively. Compared to other modern approaches, the proposed approach delivered better results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Multimedia IoT Data Compression using Deep Learning Technique 2

Multimedia IoT Data Compression using Deep Learning Techniqu...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Yathavaraj, V. Karthikeyan, N. Kousalya, A. Sindhuja, P. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India United Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

Due to the considerable complexity of the multimedia data acquired, IoT (Internet of Things) and multimedia will encounter a number of energy and communication overload limits. One well-known solution to the problem of large datasets is to use the lossy compression algorithm. However, the lower transmission rate required by current lossy compression techniques to ensure endurable apparent image/picture quality only results in a minor reduction in the amount of communicated data and, as a result, a minor reduction in power and bandwidth use. An effective firmness technique is required to strike a fair equilibrium flanked by information volume and visual degradation. A deep-learning super-resolution technique is utilized in this study to restore high-resolution photographs from damaged photos with a considerable compression ratio as input. The results of the study clearly show how the suggested approach improves the graphical fidelity of compressed and down scaled images. The suggested method reduces largely the communication transparency and energy consumption of constrained multimedia IoT framework components as a result. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers 2

EmoCNN: Unleashing Human Emotions with Customized CNN Using ...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sahana, M. Umesh, Praneetha Kodipalli, Ashwini Rao, Trupthi Department of Computer Science & Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

AntiMicrobial Resistance Prediction Using Advanced Image Encoding Techniques 1

AntiMicrobial Resistance Prediction Using Advanced Image Enc...

引用

1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Ghadekar, Premanand Jaggi, Arjun Swain, Debabrata Ghadekar, Aryan Vishwakarma Institute of Technology Department of Information Technology Pune India HCLTech CA United States Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331520540

Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their treatment approaches. The traditional machine learning models used in AMR prediction usually suffer from poor genomic sequences feature extraction and finding it hard to solve big biological problems with big data. K-mer based methods or other simpler classifiers have been able to provide certain accuracy, but these have strived when dealing with many genomic strains and variants. To address these shortcomings, deep learning models are able to learn data representation at various abstraction levels which understanding complex data of fundamental and high dimensional patterns. In this work we provide a new deep learning approach exploiting CNNs on FCGR images of microbial genomes. The sequence is represented as frequency chaos game representation making it better for the enn structural application as resnet and densenet where features are deeply embedded in classifiers. When it comes to string and image based methods it is clear that they both have their own benefits and shortcomings. String based analysis is a faster and more accurate way to analyze naked sequence data conversion, but it may not be as informative as visual presentation. Our study verified the hypothesis that it is feasible to encode genomic data for further FCGR, so that the data might be utilized as an input for machine learning and deep learning methods. The model is effective with substantial improvements with the accuracy of over 90% in test dataset. This represents a significant improvement in accuracy score with better systems for predicting antibiotic resistance among many types of microbes. © 2025 IEEE.

关键词： AntiMicrobial Resistance Prediction CARD Frequency Chaos Game Representation microbes NCBI

来源：评论

学校读者我要写书评

暂无评论

Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

引用

IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Optimistic Multi-Agent Policy Gradient 41

Optimistic Multi-Agent Policy Gradient

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zhao, Wenshuai Zhao, Yi Li, Zhiyuan Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Aalto University Finland University of Oulu Finland

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Ransomware Detection Utilizing Ensemble Based Interpretable Deep Learning Model 2

Ransomware Detection Utilizing Ensemble Based Interpretable ...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Chisty, Tanjir Alam Rahman Rahman, Md. Mahbubur University of Professionals Department of Information and Communication Technology Bangladesh Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understanding the techniques used in ransomware development and deployment is crucial to counter these threats. This study presents a stacked ensemble-based machine learning (ML) approach for ransomware detection. The ensemble model consists of Naive Bayes (NB), Random Forest (RF), and K-Nearest Neibors (KNN) as base classifiers and Artificial Neural Network (ANN) as meta-classifiers. Additionally, Explainable AI (XAI) utilizing LIME and SHAP has been applied to gain insights into prediction outcomes and validate model correctness. The use of LIME and SHAP ensures the inter-pretability of the model, which is a crucial factor in real-world implications. The research employs a publicly available dataset and evaluates the model using multiple metrics. Experimental results reveal that the proposed model achieves an impressive accuracy of 98.57%. The model is benchmarked against existing ML techniques, demonstrating its superior performance. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Pathological Analysis of Blood Cells Using Deep Learning Techniques

引用

Recent Advances in computer science and Communications 2022年第3期15卷 397-403页

作者： Ranga, Virender Gupta, Shivam Agrawal, Priyansh Meena, Jyoti Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

Introduction: Pathologists are majorly concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. However, this method has a number of limitations such as there is no standard way of diagnosis, there are certain chances of human errors and besides, it burdenizes the laboratory personnel to diagnose a large number of slides on a daily basis. Methods: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to get an insight into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When an input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are applied in accordance with the proposed algorithm, then the model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, and testing loss for the proposed model is plotted using matplotlib and trends. Discussion: The performance of the proposed model is better than the existing standard architectures and other works done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory personnel. . This can also in turn help the pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyp

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

引用

Recent Advances in computer science and Communications 2022年第1期15卷 32-39页

作者： Patel, Warish D. Patel, Chirag Patel, Monal Department of Computer Science Engineering and Technology Parul University Vadodara India Department of Computer Science BITS Edu Campus Vadodara India

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for signifi-cant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. Therefore, even for the daily activity in the life of aged people, an automatic fall detecting system and vital signs examining system have become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people Methods: In comparison with present algorithms, there are various benefits regarding privacy, suc-cess rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algo-rithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%. ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS mod-ules so that the aged people can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The results of the experiment show the ade-quacy of the proposed approach. © 2022 Bentham science Publishers.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：