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Reinforcement learning Scheduling Research for Edge Servers 6

Reinforcement Learning Scheduling Research for Edge Servers

引用

6th international conference on Computer Network, Electronic and Automation, ICCNEA 2023

作者： Han, Yonggui Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Monitoring and Control xi'An Technological University State and Provincial Joint Engineering Lab. of Advanced Network Xi'an China

ISBN: (纸本)9798350305388

The rapid development of the Internet of Things and big data has brought about massive amounts of data and the need for real-time responses, which traditional computing models can no longer meet. Edge computing comes into being, which pushes computing resources and services closer to the source of data generation so that data can be processed and analyzed closer to the user. The scheduling of siting strategies for edge servers is crucial for improving the performance of edge computing networks. Traditional scheduling methods for siting strategies are often based on rules or optimization algorithms, which lack adaptive and intelligent management of the network *** paper proposes a reinforcement learning DQN-based edge server siting policy scheduling method, which aims to achieve intelligent management and resource optimization of edge computing networks by learning the optimal policy. Experimental results show that the method has significant performance advantages in terms of delay, load balancing and energy consumption. The experiment adopts DQN algorithm, which is based on Q-learning and guides the agent to make decisions by optimizing Q-value function. In this paper, the reinforcement learning scheduling algorithm of edge server is studied and compared with other three algorithms. According to the experimental results, the proposed reinforcement learning scheduling algorithm reduces the delay by 7%, the energy consumption by 3%, and the load balancing by 20% compared with the other three algorithms. This shows that the algorithm has significant advantages in improving system performance. © 2023 IEEE.

关键词： Reinforcement learning

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An Automatic Monitoring System for Sports Training Indicators Based on Deep learning and Multiple Sensors 1

An Automatic Monitoring System for Sports Training Indicator...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Zhang, Qian Wuhan Business University Hubei Wuhan430056 China

ISBN: (纸本)9798350323795

In recent years, with the rapid development of computer and network technology, people have become increasingly concerned about health. In order to ensure health and human health, more and more people have begun to pay attention to physical exercise. However, currently many fitness monitoring systems only collect and collect exercise data, without real-time monitoring of exercise. Moreover, traditional methods for monitoring sports training indicators lack real-time performance, and the accuracy of sports testing is also not high. This article first explores deep learning, then explains some of the changes it has brought to the field of automatic monitoring system design, and then explores the important achievements and breakthroughs that can be achieved by combining deep learning algorithms in the setting of automatic monitoring systems. Using the data survey method to investigate and statistically analyze the deep learning algorithms used in the design of automatic monitoring systems, it can be seen from the survey results that the automatic detection system using deep learning algorithms has better efficiency and accuracy than the old system. Therefore, the design of deep learning algorithms and multi-sensor sports training index automatic monitoring systems plays a crucial role. © 2023 IEEE.

关键词： learning algorithms

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Parity-Based Amplitude Embedding for Rational Quantum Dataset Preprocessing 1

Parity-Based Amplitude Embedding for Rational Quantum Datase...

引用

1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Prasetiadi, Agi Mambo, Masahiro Kanazawa University Graduate School of Natural Science and Technology Kanazawa Japan Kanazawa University Institute of Science and Engineering Kanazawa Japan

ISBN: (纸本)9798350366778

Quantum dataset preprocessing for amplitude embedding is understudied despite its importance for achieving good generalization in quantum machine learning models. Amplitude embedding, known for its compression capabilities, has garnered attention in recent research. Combined with the Variational Quantum Classifier, they form a potential combination for high performance in quantum machine learning, outperforming many classical models. However, the full normalization of amplitude embedding has fundamental issues, such as inconvertibility for specific values and irrational amplitude distribution due to normalization. To overcome these challenges, we propose a novel and straightforward dataset preprocessing solution using guardian parity designed for amplitude embedding. This method ensures the normalized dataset remains recoverable to its original MinMax scaled values, simultaneously enhancing dataset quality and improving accuracy when employed in quantum machine learning models. Experimental results show that the proposed parity-based amplitude embedding significantly impacts the accuracy of machine learning models compared to the original amplitude embedding method. © 2024 IEEE.

关键词： Quantum computers

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Fraud Detection In Ethereum Transactions: A Machine learning Approach 1

Fraud Detection In Ethereum Transactions: A Machine Learning...

引用

1st international conference on Advanced computing and Emerging Technologies, ACET 2024

作者： Neogi, Anandarupa Mukhopadhyay, Disha Jaiswal, Anubhav Kumar, Ankush Misra, Bitan Techno International New Town Computer Science and Engineering Kolkata700156 India

ISBN: (纸本)9798350367720

This study explored the use of machine learning models to detect fraudulent transactions made over Ethereum, a type of cryptocurrency. The dataset included data from 9840 transactions which included both fraudulent and legitimate transactions and included various parameters such as average, minimum and maximum amounts received and sent, the number of unique addresses to and from which ether was sent and received, the time duration between the transactions and the remaining balance after the transactions. The transaction amount lies in a range of 12000.0 to 52000.0. Four machine learning models, namely Decision Tree, Random Forest Classifier, Catboost Classifier and XGBoost Classifier, are evaluated for fraud transaction detection. XGBoost has emerged as the most promising model, achieving near perfect accuracy (99.65%) and high precision, recall and F1-scores. Notably the robust performance of XGBoost suggests it's potential for accurately detecting fraudulent transactions in Ethereum. However, comparatively weaker performance is observed in models like the Decision Tree and Random Forest. This study underscores the significance of machine learning in enhancing fraud detection in Ethereum transactions and advancement of the field of cybersecurity in blockchain technologies by providing a useful and scalable fraud-detecting instrument. © 2024 IEEE.

关键词： Ethereum

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Feature Map for Quantum Data in Classification 1

Feature Map for Quantum Data in Classification

引用

1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Kwon, Hyeokjea Lee, Hojun Bae, Joonwoo School of Electrical Engineering Korea Adv. Inst. of Sci. and Tech. Yuseong-gu Korea Republic of

ISBN: (纸本)9798350366778

The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. In this work, we point out that the quantum state space is specific such that a measurement postulate characterizes an inner product and that manipulation of quantum states prepared from classical data cannot enhance the distinguishability of data points. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Cardiovascular Disease Prediction Using Ensemble learning 7

Enhancing Cardiovascular Disease Prediction Using Ensemble L...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ambade, Sharwari Chikmurge, Diptee School of Computer Engineering Mit Academy of Engineering Alandi India

ISBN: (纸本)9798350304268

The proposed ensemble learning framework integrates diverse machine learning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model's performance, a large-scale dataset comprising anonymized electronic health records from a diverse patient population is employed. The dataset includes longitudinal data, allowing for the incorporation of temporal information. Preliminary results demonstrate that the ensemble learning approach outperforms individual models in terms of predictive accuracy and stability. Moreover, the model exhibits robustness across different subpopulations, indicating its potential for generalizability and applicability in diverse healthcare settings. The accuracy metrics is being utilized as a foundation for assessing the effectiveness of all classifications. With an accuracy of 93.01% the stacking classifier has proven to be the finest model. © 2023 IEEE.

关键词： Cardiovascular disease Ensemble learning Machine learning algorithms

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Optimization Accuracy of MRI Brain Image Based Tumor Detection Using Deep learning LSTM Technique 2

Optimization Accuracy of MRI Brain Image Based Tumor Detecti...

引用

2nd international conference on Advanced computing and Communication Technologies, ICACCTech 2024

作者： Chugh, Aarti Yadav, Ranjeet Kumar Agarwal, Tushar School of Computer Science and Engineering IILM University Gurugram India Shree Guru Gobind Singh Tricentenary University Department of Computer Science and Engineering Gurugram India The Galaxy Education System Gujrat India

ISBN: (纸本)9798331519056

Benign brain tumors (BT) are the least dangerous type and arise from brain cells or cells surrounding the brain. Benign tumors grow slowly and do not spread to other body parts. Benign brain tumors have distinct borders. They put the normal brain under stress and interfere with physical and mental function. It is also referred to as a non-cancerous BT. The acquisition of magnetic resonance images introduces noise, and removing this noise is complex. BT has tentacles and diffuse structures, making precise segmentation difficult. Another critical task is to select and extract the best features and the appropriate number of training and test samples to improve the classification. Deep learning (DL) models have received much attention because feature learning happens automatically. However, it necessitates a substantial amount of computing power and memory. As a result, lightweight models with high accuracy and fast computation times were developed. This paper is present on ML and DL model for analysis of BT detection and calculated accuracy. The ML classification i.e. K-NN achieves 78.74% accuracy, LR achieves 81.53% accuracy, VC achieves 86.9% accuracy and DL LSTM achieves 94.07% accuracy. It is clearly that the LSTM technique is provides good accuracy compared to other ML technique. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Real-Time Violence Recognition in CCTV Video Surveillance Enhanced by AI Deep Technique 6

Real-Time Violence Recognition in CCTV Video Surveillance En...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331522667

Despite its many uses, deep learning (DL) is a key tool in the machine learning (ML) community’s toolbox. Numerous techniques have been developed to identify violence and criminal activity on video since many crimes take place in public areas that are not adequately monitored. The significance of automatic violence detection in surveillance video studies has grown. They do, however, have several drawbacks and frequently rely on particular circumstances. This article presents a method for detecting aggression in surveillance films that is based on convolutional neural networks (CNNs). Accuracy is increased by applying both deep learning and machine learning methods as advised. Performance assessments showed how effectively the suggested approach identifies violent content in video. Test results show that the future solution works better than current approaches for classifying violent then criminal material in films. The models that were previously trained InceptionV3, ResNetV2, Inception_ResNetV2, Violence_Net, and the suggested vioNet remained trained on four datasets. The following were the classification outcomes of the endorsement data for each model: VioNet model 99.72%, InceptionResNetV2 90%, ViolenceNet pseudo-97, and InceptionV3 92%. Due to its easier integration of feature maps, the Dense_Net prototypical was selected for our application arrangement instead of the more intricate Inception or InceptionResNet models. Compared to earlier iterations, its sturdy construction provides greater accuracy and performance with fewer filters and settings. © 2025 IEEE.

关键词： Convolutional neural networks

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Graph based Clustering Techniques 15

Graph based Clustering Techniques

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pawar, Vaishali Chandwadkar, Radhika MVPS's KBTCOE Computer Engineering Department Nashik India

ISBN: (纸本)9798331300579

Clustering is widely used technique to find hidden, meaningful patterns in the given dataset, called as clusters. It is grouping of data into groups of similar objects. The objective is that the objects within a group should be similar to one another and different from the objects in other groups. Clustering plays an excellent role in data mining applications, biology, science, pattern recognition, social networking, machine learning, web analysis, CRM, marketing, medical diagnostics, and many others. A variety of clustering algorithms have emerged that meet these requirements and were successfully applied to many data mining and pattern recognition problems. Due to their great representational power, Graphs can handle huge databases with large attributes. Hence Graphs play a vital role in clustering applications. © Grenze Scientific Society, 2024.

关键词： Clustering data mining machine learning pattern recognition social networking

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Integrating Multi-Omics Data for Advanced Diabetes Prediction and Understanding 7

Integrating Multi-Omics Data for Advanced Diabetes Predictio...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mishra, Soumya Ranjan Dash, Sachikanta Padhy, Sasmita Kumar, Naween Dash, Yajnaseni Giet University Gunupur India Giet University Department of Cse Gunupur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Bhopal India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Bennett University School of Artificial Intelligence Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Diabetes is a major global health issue, requiring new approaches to understanding its complex etiology and improving predictions of effective management and treatment. Current methods typically use centralized models, which have privacy concerns and cannot fully capture correlations in multimodal datasets. To address these problems, we introduce a new model using integrated learning and causal theory. Our system uses three datasets: the Pima Indian Diabetes Dataset (PIDD), 130 U.S. Appl. Clinical Diabetes Dataset, and physiological summaries. For analysis in our proposed method, we use various machine learning methods with ensemble method. Here we achieve an accuracy of 93% for PIDD 92% for US 130 clinical datasets and 90% for physically collected datasets. By integrating genetic, proteomic, and metabolomic data, we better understand diabetic mechanisms and potential therapeutic targets. Our model addresses the limitations of current methods, improves prediction accuracy, and data privacy, and provides insight into the aetiology of diabetes. This approach provides a strong foundation for future study. © 2024 IEEE.

关键词： Adversarial machine learning

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