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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4138 条记录，以下是311-320 订阅

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machine learning-based Citrus Plant Disease Detection and Management System using computer Vision 4

Machine Learning-based Citrus Plant Disease Detection and Ma...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Srinivasa Revanth Krishna, A. Sri Siva Sandeep Reddy, T. Dheepthi, R. Hindustan Institute of Technology and Science Computer Science and Engineering India

ISBN: (纸本)9798350300093

this article presents a novel method for identifying and managing diseases that affect citrus fruits by utilizing cutting-edge computer vision and machine learning techniques. the method is presented in this article. the proposed system utilizes a Convolutional Neural Network (CNN) model to extract features from images and classify them as healthy or diseased. Following this, a Random Forest algorithm is used to make the final prediction based on the extracted features. the system enables farmers to easily upload images of their citrus fruits from mobile or web-based platforms, enabling instant diagnosis of diseases and providing effective management plans to address the issue. the design of the proposed system is centered on enhancing the performance of the model in recognizing patterns that are associated with healthy and diseased fruits. In addition to this, it features an automatic management plan generation and alert system, both of which are designed to prompt farmers to take prompt actions for the prevention and control of diseases. the purpose of the proposed system is to deliver an instrument that is both effective and simple to use for the diagnosis and treatment of diseases, which will ultimately result in increased crop yields and increased profitability for citrus growers. the agricultural sector as a whole intends to gain significantly from the successful implementation of this system, which has the potential to bring about significant improvements. © 2023 IEEE.

关键词： computer vision

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Artificial Intelligent Player for Bullet Hell Games Based on Deep Q-Networks 4

Artificial Intelligent Player for Bullet Hell Games Based on...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Tian, Dongxin Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798400709449

this study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. the trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. these challenges may provide directions for future research. © 2023 ACM.

关键词： Deep learning

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Embodied Intelligent Driving: Key Technologies and applications 4

Embodied Intelligent Driving: Key Technologies and Applicati...

引用

4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Wang, Yadong Chen, Sihan Li, Zhiwei Shen, Tianyu Wang, Kunfeng Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798350349252

Embodied intelligence emphasizes direct interaction between machines and the physical world, enabling intelligent agents to exhibit intelligent behaviors and autonomous evolution through the interplay of the brain, body, and environment. this paper discusses key technologies involved in embodied intelligent driving, based on the core requirements of embodied intelligence and autonomous driving, focusing on two core aspects: embodied perception and execution, and embodied learning and evolution. the development status and existing issues of these technologies are analyzed. Additionally, the paper elaborates on datasets and practical system applications related to embodied intelligent driving. Subsequently, it presents reflections and prospects for this field, considering the significant role and application potential of parallel robot, aiming to promote the application of embodied intelligent driving in highly dynamic real-world scenes. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of soil moisture in Inner Mongolia's League based on machine learning 4

Prediction of soil moisture in Inner Mongolia's League based...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhu, Yu Jing, Xiangnan Ding, Aoyun Chizhou University School of Big Data and Artificial Intelligence Chizhou China City University of Hefei School of Economics and Management Hefei China City University of Hefei School of Civil Engineering Hefei China

ISBN: (纸本)9781510672505

Traditional mathematical statistics and machine learning models have limitations in soil moisture prediction. this study offers an algorithm based on XGBoost under correlation for soil moisture prediction at a depth of 10 cm, which can achieve an accuracy of 99.29 %. It has a greater advantage over several other machine learning models. the model is also optimized by Grid Search and Randomized Search for the model hyperparameter, and its model is proven to have good generalization ability and robustness. © 2023 SPIE.

关键词： Soil moisture

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Quantifying Outlierness of Funds from their Categories using Supervised Similarity 23

Quantifying Outlierness of Funds from their Categories using...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Desai, Dhruv Dhiman, Ashmita Sharma, Tushar Sharma, Deepika Mehta, Dhagash Pasquali, Stefano Black Rock Inc New York NY 10055 USA Black Rock Inc Gurgaon Haryana India

ISBN: (纸本)9798400702402

Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning. As a result, a (unintended) miscategorization or lack of precision can significantly impact allocation decisions and investment fund managers. Here, we aim to quantify the effect of miscategorization of funds utilizing a machine learning based approach. We formulate the problem of miscategorization of funds as a distance-based outlier detection problem, where the outliers are the data-points that are far from the rest of the data-points in the given feature space. We implement and employ a Random Forest (RF) based method of distance metric learning, and compute the so-called class-wise outlier measures for each data-point to identify outliers in the data. We test our implementation on various publicly available data sets, and then apply it to mutual fund data. We show that there is a strong relationship between the outlier measures of the funds and their future returns and discuss the implications of our findings.

关键词： Mutual funds Similarity learning Outlier Detection

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Levy Swallow Swarm Optimization with machine learning-based Intrusion Detection in IIoT 4

Levy Swallow Swarm Optimization with Machine Learning-based ...

引用

4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alzahrani, Abdulrahman Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia

ISBN: (数字)9781837240258

ISBN: (纸本)9781839539954

With the recent evolution ofIndustry 4.0, integrated devices and sensors started to generate dynamic, extensive, and heterogeneous in Industrial Internet of things (IIoT) environments. the immense heterogeneous data probably raises the difficulties of data analysis procedures and security risks. As IIoT develops, cyber-attacks are more complex and diverse, making current anomaly detection methods’ operations less effective. Intrusion Detection System (IDS) had a major contribution to protecting IoT systems from cyberattacks by constructing complementing classical preventive security measures (e.g., encryption, authentication, and authorization). therefore, in this paper, we present a Levy Swallow Swarm Optimization with machine learning-based Intrusion Detection (LSSOML-ID) technique in IIoTenviornments. the presented LSSOML-ID technique focuses on the identification and classification of intrusions in IIoT environments. To achieve this, the LSSOML-ID technique uses a Z-score normalization algorithm to scale the input data into a uniform format. For feature selection purposes, the LSSO algorithm was used which reduces the number of features involved in the classification process. Finally, the Aquila optimizer (AO) algorithm with an extreme learning machine-based autoencoder (ELM-AE) model is proficiently employed for intrusion classification. To exemplify the significant outcomes of the LSSOML-ID technique, a thoroughset of experiments areconducted. the comprehensive outcomes highlighted the betterment of the LSSOML-ID approach over recent methods. © the Institution of Engineering & Technology 2023.

关键词： Network intrusion

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Grouped Federated learning Algorithm Based on Non-IID Data 4

Grouped Federated Learning Algorithm Based on Non-IID Data

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Li, Zhongxin Zhang, Jianfei School of Computer Science and Technology Changchun University of Science and Technology China

ISBN: (纸本)9798400709449

Federated learning is a new machine learning paradigm in which multiple clients collaborate to train a machine learning model while protecting local data privacy. Client-side non-identically and non-independently distributed (Non-IID) data is one of the most important challenges for federated learning algorithms. the classification accuracy of the federated learning model drops drastically due to the weight dispersion caused by data heterogeneity. In this work, we adopt a way of dividing client data that is closer to real applications. We propose a federated learning algorithm based on rearranging all clients(FedRC). FedRC sets up a novel within-group training approach and a server-side pre-aggregation policy to address the impact of Non-IID on the model. In addition, we test FedRC on a simulated dataset and the experimental results demonstrate that FedRC outperforms the baseline algorithm and significantly improves the performance on Non-IID data. © 2023 ACM.

关键词： learning algorithms

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Aquaponics and Smart Hydroponics Systems Water Recirculation Using machine learning 4

Aquaponics and Smart Hydroponics Systems Water Recirculation...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300888

Hydroponics and aquaculture (fish farming) are combined in the innovative, cunning, and sustainable agriculture method known as aquaponics to grow vegetables in a cooperative relationship. When done properly, aquaponics can produce nutritious organic veggies with little water and chemical-resistant lettuce varieties that can thrive in hydroponic systems. With the use of automated robotics, the Internet of things, and smart hydroponics, farmers can keep an eye on the health of their plants, root zones, and surrounding ecosystems in real time. the long-term process of aquaponics involves growing plants and fish in the same habitat. In contrast, hydroponically growing plants is possible without the need for soil. Depending on the circumstance, one of these tactics may be fruitful and advantageous. It's crucial to know the differences between aquaponics and hydroponics, as some plants do better with one method than the other. By adding virtual pieces to the actual environment or building wholly virtual surroundings, it is possible to simulate aquaponics tanks, grow beds, piping, and equipment. © 2023 IEEE.

关键词： Hydroponics

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Intelligent Collaborative Robot Based on DMHF-CNN+YOLOv7 and Soft Q-learning in Non-Standard Industrial Parts Assembly 4

Intelligent Collaborative Robot Based on DMHF-CNN+YOLOv7 and...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Zhang, Xiaodong Qin, Minghua Cheng, Tao Liu, Xinjie Xue, Rurui Hu, Bin Li, Yun Xiong, Gang Dong, XiSong Yantai Institute of Technology China Qingdao Academy of Intelligent Industries China State Key Laboratory of Multimodal Artificial Intelligence Systems China Chinese Academy of Sciences Institute of Automation China

ISBN: (纸本)9798350349252

the aim of this study is to explore a novel intelligent collaborative robot system that incorporates DMHF-CNN+YOLOv7 target detection technology, hybrid continuous-discrete reinforcement learning motion planning, and Soft Q-learning-based intelligent grasping control technology. Experimental results show that the system exhibits good performance and stability in non-standard industrial parts assembly tasks. the accuracy of target detection reaches more than 95 percent, the accuracy of motion planning and the success rate of grasping are significantly improved, and the assembly time and the cost of human intervention are effectively reduced. the research results in this paper provide strong support for the application of intelligent manufacturing technology in actual production. © 2024 IEEE.

关键词： Adversarial machine learning

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A Recent Systematic Review of Ransomware Attack detection in machine learning techniques 4

A Recent Systematic Review of Ransomware Attack detection in...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Ahmad, Samsiah Zulkifli, Zalikha Nasarudin, Nur Hasni Imran, Mohamed Ariff, Mohamed College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Computing Sciences Studies Tapah Malaysia

ISBN: (纸本)9798350318432

Ransomware attacks have emerged as a significant threat to organizations and individuals, causing substantial financial and operational damages worldwide. With the increasing sophistication and frequency of ransomware attacks, it is crucial to develop effective detection mechanisms to identify and mitigate these threats. machine learning techniques have gained prominence in detecting ransomware attacks due to their ability to analyze large volumes of data and identify patterns indicative of malicious activities. this systematic review aims to provide a comprehensive analysis of the existing literature on ransomware attack detection using machine learning techniques. © 2023 IEEE.

关键词： Malware

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