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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441-450 订阅

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Defending Against App-Layer DDoS: Advanced Machine learning Security 4

Defending Against App-Layer DDoS: Advanced Machine Learning ...

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Salmi, Salim Oughdir, Lahcen Affar, Anas El Sidi Mohamed Ben Abdellah University Ensa Engineering Systems And Applications Laboratory Fez Morocco Sidi Mohamed Ben Abdellah University Department Of Mathematics Physics And Informatics Fp Taza Morocco

ISBN: (纸本)9798350309508

Distributed denial-of-service attacks in the application layer (DDoS) pose a significant threat to online services, leading to significant financial losses and reputational damage. this intriguing research paper presents a novel automated detection approach to identify dangerous application layer DDoS attacks. Four machine learning algorithms, including random forest, decision tree, K-nearest neighbor and naive Bayes, were tested on three attack scenarios: benign (legitimate), DoS Slowloris and DoS Hulk using network traffic data feature extraction from deep packet inspection. the results showed that the Random Forest classifier excelled in all cases with incredible accuracy, precision recall, and F1 scores. the proposed approach provides a robust and efficient solution for mitigating application layer DDoS attacks, enhancing the security of online services, and reducing the impact of DDoS attacks. © 2024 IEEE.

关键词： Decision trees

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Research on the Application of AI Intelligent Model of Computer Deep learning in Natural Language Processing 4

Research on the Application of AI Intelligent Model of Compu...

引用

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

作者： Shen, Miaofang College of Information Engineering Guangzhou Vocational and Technical University of Science and Technology Guangzhou510080 China

ISBN: (纸本)9798350359589

the relationships between entities in a document are extracted according to natural language processing methods. Deep neural network is used to recognize the required multi-label text. According to the general specification, the system is optimized, and the design and implementation of the system are obtained. this project explores four major NLP modes such as ALBERT, RNN Search, BERT-CRF, Text ING based on the high-performance hardware of the Centeno platform. According to the element relation, tree structure and network structure, a general MNet construction method is proposed. the extracted correlation information is used to determine whether the matching conditions of each security requirement template are established, and then the final set of security requirement templates is screened. the extracted security requirements are modeled and instantiated in this way. Simulation results show that the model can deal with semantic dependency and human-computer interaction in complex systems. By analyzing the semantics of the operation interface in SCADA system, it is transformed into a general MNet construction, which lays a foundation for realizing the semantic analysis of users. © 2024 IEEE.

关键词： data acquisition deep learning Intelligent model natural language processing system control

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Role of thermal Images in Various applications of Computer Vision 4

Role of Thermal Images in Various Applications of Computer V...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Gupta, Ravina Jain, Sarika Kumar, Manoj AIIT Amity University Noida India EIS University of Wollongong Dubai United Arab Emirates

ISBN: (纸本)9798350341126

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. this paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. this study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector Machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets. © 2023 IEEE.

关键词： Object detection

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FedAVO: Improving Communication Efficiency in Federated learning with African Vultures Optimizer 48

FedAVO: Improving Communication Efficiency in Federated Lear...

引用

48th Annual IEEE international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Hossain, Md Zarif Imteaj, Ahmed Shahid, Abdur R. Southern Illinois Univ Sch Comp Carbondale IL 62901 USA Secur Privacy & Intelligence Edge Devices Lab SPEED Lab Carbondale IL 62901 USA

ISBN: (纸本)9798350376975;9798350376968

Federated learning (FL) has recently experienced tremendous popularity due to its emphasis on user data privacy. However, the distributed computations of FL can result in constrained communication and drawn-out learning processes, necessitating the client-server communication cost optimization. the ratio of chosen clients and the quantity of local training passes are two hyperparameters that have a significant impact on the performance of FL. Due to different training preferences across various applications, it can be difficult for FL practitioners to manually select such hyperparameters. In this paper, we introduce FedAVO, a novel FL algorithm that enhances communication effectiveness by selecting the best hyperparameters leveraging the African Vulture Optimizer (AVO). Our research demonstrates that the communication costs associated with FL operations can be substantially reduced by adopting AVO for FL hyperparameter adjustment. through extensive evaluations of FedAVO on benchmark datasets, we identify the optimal hyperparameters that are appropriately fitted for the benchmark datasets, eventually increasing global model accuracy by 6% in comparison to the state-of-the-art FL algorithms (such as FedAvg, FedProx, FedPSO). the code, data, and experiments have been made publicly available on our Github repository(1).

关键词： Federated learning African Vulture Optimizer Communication efficiency Local model Global model Convergence

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A gait control method for bipedal robots based on reward function optimization 4

A gait control method for bipedal robots based on reward fun...

引用

4th international conference on Intelligent Communications and Computing

作者： Pei, Ruoyan Ma, Pengge Zhang, Tong Li, Lan Zhengzhou Univ Aeronaut Zhengzhou Peoples R China Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9798331530372;9798331530365

the intelligent control of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult for robots to maintain walking stability and simulate human gait at the same time for its excessive attention on stability. Compared with the traditional deep reinforcement learning algorithm, the DDPG algorithm has the ability to handle continuous action space and high-dimensional state space, and has a wide application prospect in many practical problems. We proposed a bipedal robot control method based on reward function optimization to enable the robot to achieve both stable high-speed movement and human gait imitation. this paper combines the physical characteristics of the bipedal robot to establish a control system based on the DDPG algorithm. At the same time, the reward function is designed to guide the robot to learn the correct walking strategy. through the comparative test, the weight limit ratio of each reward function for the bipedal robot to stably increase the speed is given. the simulation results show that the method proposed in this paper has good practicality and effectiveness.

关键词： Bipedal robot gait control deep reinforcement learning DDPG algorithm reward function

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A Trust Based Node Identification and Location optimization for WSN using Cloud Infrastructure 4

A Trust Based Node Identification and Location Optimization ...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Sharma, Vishal Agarwal, Varsha School of Media Film &television Studies Iimt University Uttar Pradesh Meerut India Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

the deployment of wireless sensor networks (WSNs) into the environment can increase the awareness of the environment in most cases but it is usually restricted by limited energy and computing resources in the network. To address this limitation, cloud infrastructure can be used as a platform for location optimization. With effective location optimization, the cloud infrastructure can use its computing capabilities to minimize the number of sensor displacements while maintaining the desired coverage of the WSN. this will allow the WSN nodes to extend the lifetime and make more efficient use of their limited energy and computing resources while still providing the necessary coverage. Location optimization algorithms can be used to determine the optimal locations for the network nodes. these algorithms consider the spatial characteristics of the environment and prioritize the placement of nodes based on their expected utility. Several methods have been proposed for location optimization based on the cloud infrastructure, such as Reinforcement learning, Differential Evolution, and Swarm optimization techniques. these techniques were compared in terms of their accuracy and scalability in optimizing a WSN's coverage performance. the results showed that Differential Evolution outperformed the other techniques in terms of accuracy and Swarm optimization was the best for scalability. the use of the cloud for location optimization for WSNs can help extend the lifetime of the network and provide a viable solution for long-term data collection. © 2023 IEEE.

关键词： Scalability

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the application of large-scale MIMO detection algorithm in 5G and beyond 5G 4

The application of large-scale MIMO detection algorithm in 5...

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4th international conference on Signal Processing and Machine learning, CONF-SPML 2024

作者： Tang, Qiteng Harbin Institute of Technology Shenzhen Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781510674721

In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. this paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. these two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. this method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. they offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology. © 2024 SPIE.

关键词： Machine learning

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LexSage: Multi-Task optimization in Legal Large Language Model applications 4

LexSage: Multi-Task Optimization in Legal Large Language Mod...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Han, Lin Zhang, Yang Gao, Wei Li, Xiaowen National Supercomputing Center in Zhengzhou Zhengzhou China Zhengzhou University Zhengzhou China

ISBN: (纸本)9798331531225

With the increasing complexity of tasks in the legal domain, traditional methods struggle to meet the demands of multi-task scenarios and face significant bottlenecks in task accuracy and efficiency. To address this, we propose LexSage, a legal large language model that leverages the one-shot capability of large language models (LLMs). By utilizing different prompt templates to assist in data generation and applying data augmentation techniques to further expand the dataset, LexSage performs instruction fine-tuning on the Qwen2.5-7B model with a comprehensive legal dataset. through knowledge sharing and structural optimization across tasks, LexSage achieves a synergistic effect during multi-task fine-tuning, effectively integrating and enhancing the knowledge and expressive power across different tasks. Evaluation on LawBench shows that LexSage exhibits significant performance improvement in multiple tasks, including 25.6% improvement in the case analysis task compared to the GPT4 model, and 15.6% improvement in the law recitation task compared to the hanfei legal large language model. the experimental results show that LexSage demonstrates a strong comprehensive processing capability in legal intelligence applications, providing new ideas and technical support for the intelligent development of the legal field. © 2024 IEEE.

关键词： Multi-task learning

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Reliability Assessment of Solder Ball Joints Using Finite Element Analysis and Machine learning techniques 10

Reliability Assessment of Solder Ball Joints Using Finite El...

引用

10th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Reddad, H. Zemzami, M. El Hami, N. Hmina, N. Nguyen, N-Q Ibn Tofail Univ Adv Syst Engn Lab ENSA Kenitra Morocco Mohamed V Univ ENSAM Rabat Morocco Univ Ibn Tofail Sci & Engn Lab ENSA Kenitra Morocco Univ Technol Troyes Chaire Connected Innovat Comp Sci & Digital Soc L Troyes France

ISBN: (纸本)9798350373981;9798350373974

this paper presents a comprehensive approach for reliability assessment and optimization of ball joints by combining finite element analysis (FEA) method with machine learning algorithms, in particular by exploiting the power of the XGBoost machine learning algorithm. FEA simulations were carried out using ANSYS to generate a dataset encompassing various solder joint geometrical parameters configurations. Key input parameters, including geometric dimensions, were identified by sensitivity analysis and used as features for training the XGBoost model. the trained model demonstrated solid performance. Feature importance analysis revealed critical factors influencing solder joint reliability and provided insights for optimization strategies. this research provides practical recommendations for optimizing solder joint and material design to improve reliability and performance in electronic packaging applications.

关键词： Reliability analysis

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

4th International Conference on Intelligent Engineering and ...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

ISBN: (纸本)9798350341126

the proceedings contain 68 papers. the topics discussed include: app based implementation of modern agriculture utilities for farmers;application of intelligent computing (IC) in human resources (HR) analytics: towards better tomorrow;artificial intelligence based chatbot for promoting equality in high school advising;assessment of human activity recognition based on impact of feature extraction prediction accuracy;biometric and RFID passive tag based student identification system for secure attendance management;blockchain and federated learning enabled smart traffic management system for smart cities;climatic temperature forecasting with regression approach;covid-19 detection on X-ray image using deep learning;critical review on applications of self-healing concrete in reinforced concrete structures;decipherable for artificial intelligence in Medicare: a review;dementia identification using machine learning algorithms: comparative analysis;and design and implementation of an easy-to-use tracking device for logistic applications.

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