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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是301-310 订阅

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Optimizing search advertising strategies: integrating reinforcement learning with generalized second-price auctions for enhanced ad ranking and bidding 4

Optimizing search advertising strategies: integrating reinfo...

引用

4th international conference on Advanced algorithms and Signal Image Processing, AASIP 2024

作者： Zhou, Chang Zhao, Yang Cao, Jin Shen, Yi Cui, Xiaoling Cheng, Chiyu Columbia University New York United States Dallas United States University of Michigan Ann Arbor United States San Jose United States University of California Irvine United States

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

this paper explores the integration of strategic optimization methods in the context of search advertising, focusing on ad ranking and bidding mechanisms within e-commerce platforms. Employing a combination of reinforcement learning and evolutionary strategies, we propose a dynamic model that adjusts to varying user interactions and optimizes the balance between advertiser cost, user relevance, and platform revenue. Our results suggest significant improvements in ad placement accuracy and cost-efficiency, demonstrating the model’s applicability in real-world scenarios. © 2024 SPIE.

关键词： Marketing

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Container Acceleration Method Based on Image Block Granularity optimization 4

Container Acceleration Method Based on Image Block Granulari...

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4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Deng, Guanghong Hu, Huiyang Wuhan Digital Engineering Institute System Platform Department Wuhan China

ISBN: (纸本)9798350359589

In the DevOps development mode, a significant amount of redundant data often accumulates in the image file layer. the repeated loading of this redundant data consumes valuable storage and network resources in the cluster, impacting the efficiency of cluster scheduling and Pod creation. To address the data redundancy caused by container images, this paper proposes a block granularity optimization method for image storage. the image file layer is divided into multiple equally sized data blocks, with a weak check code calculated for each block to validate its content. Additionally, a strong check code is used to verify the integrity of each data block and determine if the internal data of the file layer can be reused, reducing data redundancy across different versions. Experimental results demonstrate that the algorithm effectively reuses identical data within the file layer, resulting in a more than 4.2 times increase in Pod creation speed. © 2024 IEEE.

关键词： container image data redundancy Kubernetes Pod creation efficiency scheduling sliding blocking

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Critical Disaster Prediction using Machine learning algorithms 4

Critical Disaster Prediction using Machine Learning Algorith...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Nirmala, E. Suresh, M. Maragatharajan, M. Dadlani, Riya VIT Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India VIT Bhopal University School of Computing Sciences and Artificial Intelligence Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798331515720

Prediction of earthquake is a challenging factor in the early warning system. there are a number of geographical locations that cause earthquake very often due to climatic changes. Earthquake is one among the natural disaster that root cause for substantial damage and risk to human life. this article discusses in detail about the earthquake prediction scheme constructed through machine language with automated algorithmic implementation over regression, random forest and SVM model. this accuracy prediction with comparative analysis determines the suitable model for prediction accuracy of earthquake. the objective of this research study predicts the accuracy based on magnitude, magnitude and location or region, number of region and depth of earthquake parameters from the Kaggle dataset of 18029 values. Hence, prediction with three different model generates the possible accurate prediction of 99.9% to predict the magnitude and probability of earthquake occurring in a precise area (California) from the significant data of that county using numerous Machine learning (ML) models. © 2024 IEEE.

关键词： Prediction models

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A technique utilizing Machine learning and Convolutional Neural Networks (CNN) for the identification of SQL Injection Attacks 4

A technique utilizing Machine Learning and Convolutional Neu...

引用

4th international conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Setiyaji, Andri Ramli, Kalamullah Hidayatulloh, Zulkifli Yasin Budhi Dharmawan, G.S. Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Depok Indonesia Center of Network Communications Meteorological Climatological and Geophysical Agency Jakarta Indonesia

ISBN: (纸本)9798350376111

Analyzing logs can aid in debugging or optimizing system performance. A comprehension of system efficacy is frequently linked to an awareness of the manner in which the system's resources are utilized. SQL injection attacks, specifically targeting web applications, have become a significant risk in the realm of cybersecurity. SQL injection attacks primarily result in the unauthorized disclosure of user data, allowing for data manipulation, updating, and deletion within web applications. Conventional methods employed to mitigate SQL injections encompass rule-based matching and other associated techniques that have a narrow scope in addressing only a few types of SQL injections. this research study examines code specifically designed to identify and prevent SQL injection attacks. Various supervised Machine learning techniques and Convolutional Neural Network (CNN) models are employed to assess the model. the CNN model that was suggested exhibited a notable accuracy rate of 91% and outperformed other machine learning algorithms. Furthermore, the report gives a comprehensive analysis of several machine learning algorithms employed for the detection of SQL injection attacks. the study evaluates the effectiveness of different methods in detecting SQL injection attacks using F1 Score, accuracy, recall and precision metrics. Also compares the performance of machine learning and CNN models. © 2024 IEEE.

关键词： Convolutional neural networks

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Time Series Forecasting Based on Combinatorial Models and optimization 7

Time Series Forecasting Based on Combinatorial Models and Op...

引用

7th international conference on Advanced algorithms and Control Engineering, ICAACE 2024

作者： Sun, Zichang Shandong University Weihai China

ISBN: (纸本)9798350361445

As a key tool for understanding data changes over time, time series analysis plays a key role in a variety of disciplines and applications, and its in-depth study is of great significance. In this paper, singular spectrum analysis (SSA) is used to decompose and reconstruct the time series. Based on these decomposition data, three deep learning methods, namely, recurrent neural network (RNN), long-short-term memory neural network (LSTM), and BP neural network (BPNN), are further applied for time series prediction. the performance of these neural networks is optimized through the use of the Gray Wolf optimization (GWO) algorithm to adjust their weights, and sensitivity analysis is performed to assess the robustness of the models. the innovation of this method lies in its combination of a variety of advanced signal processing techniques, deep learning algorithms, and optimization strategies, thus achieving significant results in wind speed prediction. © 2024 IEEE.

关键词： Long short-term memory

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Ransomware Classification: A Comparative Analysis of ML algorithms 4

Ransomware Classification: A Comparative Analysis of ML Algo...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Waghmare, Shweta Bajeja, Sunil Marwadi University Gujrat Rajkot India Marwadi University Faculty of Computer Application Gujrat Rajkot India

ISBN: (纸本)9798331518981

the increasing prevalence of ransomware, malware, and malicious cyberattacks poses a significant threat to computer networks, data centers, websites, mobile applications, and industrial environments across various business sectors. Ransomware, a type of malicious software, encrypts victims' files and demands a ransom for the decryption key, creating widespread disruption for individuals and organizations globally. As ransomware continues to evolve and grow in sophistication, the timely and accurate detection of ransomware has become essential. this study aims to address this critical issue by providing a comprehensive analysis of ransomware variants and exploring the use of machine learning techniques to enhance detection capabilities. the research employs a systematic review of existing ransomware detection methods and evaluates the potential of machine learning models to classify and detect different ransomware variant groups. the findings highlight the strengths and limitations of various approaches and demonstrate the effectiveness of machine learning in identifying ransomware patterns. this study's insights are expected to contribute to the development of more robust and adaptive defense mechanisms against the ever-evolving threat of ransomware. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep learning-based Study of Terrain Classification and Distance optimization for Transmission Lines 4

A Deep Learning-based Study of Terrain Classification and Di...

引用

4th international conference on Smart Grid and Energy Internet, SGEI 2024

作者： Wan, Xin He, Rui Yan, Tianyou Xu, Binbin Chen, Lixia Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou510630 China

ISBN: (纸本)9798350368314

the current terrain classification of transmission lines is generally target classification or fixed-point classification, which lacks comparability and leads to the reduction of the optimization frequency of a single transmission line, so we propose to study the terrain classification and distance optimization of transmission lines based on deep learning. According to the current measurement, data acquisition and terrain feature extraction are carried out first to improve the contrast of optimization in a multistage way, and the multistage classification of terrain and preliminary planning of paths are carried out. Based on this, a deep learning transmission line distance optimization model is designed, and dynamic correction scheduling is adopted to realize the optimization process. the test results show that the deep learning transmission line terrain classification and distance optimization method is higher compared with the renewable energy transmission line protection method under the prediction of traveling wave arrival time, and the efficient hybrid strategy of FFT and fuzzy logic technology in the transmission line fault diagnosis method, which indicates that the optimization effect of the present study has a stronger coverage, better processing speed, and a more accurate completion of the preset tasks. ©2024 IEEE.

关键词： Contrastive learning

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A PSO-Assisted Reinforcement learning Algorithm for Job Shop Scheduling 6

A PSO-Assisted Reinforcement Learning Algorithm for Job Shop...

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6th international conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Yue, Peng Jin, Yaochu Shi, Qi Dai, Xuewu Cui, Dongliang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China Northumbria Univ Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9798350377859;9798350377842

the Job Shop Scheduling Problem (JSSP) is a critical and complex optimization challenge in manufacturing. Recently, Reinforcement learning (RL)-based methods have garnered significant attention due to their promising performance in improving scheduling efficiency. However, traditional RL algorithms often suffer from inefficiencies in exploring the vast solution space, resulting in suboptimal policies. this paper introduces a novel Reinforcement learning algorithm assisted by particle swarm optimization (PSO), called PSO-RL, to address these limitations. We design a multiple-particle searching framework for the RL algorithm, where multiple solutions can be improved synchronously by an RL model. During the training process, PSO is periodically applied to guide the worst solutions towards the global and local optima identified so far. By integrating PSO's global search capability with RL's adaptive learning, our PSO-RL algorithm achieves a balanced exploration-exploitation trade-off. Experimental results on benchmark JSSP instances demonstrate that PSO-RL outperforms state-of-the-art methods, yielding lower makespans. the adaptability of the PSORL framework makes it a versatile tool for various industrial applications, enhancing the performance of production planning and intelligent manufacturing systems.

关键词： Job Shop Scheduling Particle Swarm optimization Reinforcement learning Hybrid algorithms

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A Phishing URL Detection Model based on Horse Herd optimization and Random Forest algorithms 4

A Phishing URL Detection Model based on Horse Herd Optimizat...

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4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Hemannth, P. Chinta, Mukesh Satya, S. Sarat Devasena, P. Sri Aneelaja Velagapudi Ramakrishna Siddhartha Engn Coll Comp Sci & Engn Dept Vijayawada Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

Phishing attacks continue to be a notable threat to network and information security. they plan to expose user information and privacy, such as login credentials, passwords, credit card numbers, and other details, by tricking internet users into thinking they are the real deal. For the detection of phishing websites, Machine learning (ML) techniques have been progressively used, because of their abilities like to learn from and adapt to complex designs and features. A new approach for detecting phishing websites using ML techniques is proposed that incorporates the URL structure. the Horse Herd Optimisation Algorithm is used to determine the features, and the suggested method is tested on a dataset of websites with phishing threats. In the context of network and information security, these techniques are employed to identify websites with phishing threats. the objectives include collecting a new dataset, extracting pertinent features, and addressing the challenges of imbalanced data and adversarial attacks in phishing detection. the findings can assist security professionals and researchers in identifying the techniques that are best suitable for improving phishing detection and prevention.

关键词： shing Ransomware Horse Herd Algorithm Detection Cybersecurity Random Forest

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Research on key algorithm optimization strategy of oscillographic blood pressure measurement 4

Research on key algorithm optimization strategy of oscillogr...

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2024 4th international conference on Artificial Intelligence and Industrial Technology applications, AIITA 2024

作者： Wang, Long Luo, Yiqun Tang, Zhenghua School of Physics and Electronic and Electrical Engineering Xiangnan University Chenzhou423000 China

In this paper, adaptive filtering and wavelet de-baseline drift algorithms are proposed for the signal preprocessing of the oscillographic blood pressure measurement algorithm. On feature extraction, fuzzy logic classification and dynamic programming tracking methods are designed. the blood pressure model of support vector machine and adaptive neural network is constructed. Active learning and cross-domain modeling are introduced to correct individual differences. this study comprehensively optimizes the key algorithm of oscillographic blood pressure measurement, and significantly improves the measurement accuracy, stability, and individual adaptability of the system. © Published under licence by IOP Publishing Ltd.

关键词： Adaptive filtering

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