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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7176 条记录，以下是1271-1280 订阅

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HMM-Based Market Regime Detection with RL for Portfolio Management

HMM-Based Market Regime Detection with RL for Portfolio Mana...

引用

intelligent data and Security (IDS), IEEE international conference on

作者： Jean Ndoutoumou Zining Yin Xiaochang Cheng Numeraxial LLC New York NY Dept. Computer Sciences Columbia University New York NY Financial Engineering New York University New York NY

ISBN: (数字)9798331596613

ISBN: (纸本)9798331596620

this paper studies the impact of combining a Hidden Markov Model (HMM) with a Reinforcement learning (RL) framework, implemented within FinRL, to enhance algorithmic trading. Our objective is to improve trading performance and realism by dynamically adapting to market regimes. Leveraging a portfolio of 30 Dow Jones stocks, we analyze the impact of regime awareness by comparing hybrid RL-HMM agents to standard FinRL algorithms. We observe that RL-HMM outperforms baseline models in Sharpe Ratio, annual returns, and other risk-adjusted performance metrics.

关键词： Adaptation models Heuristic algorithms Hidden Markov models Reinforcement learning Robustness Security Standards Portfolios

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Satellite Image Denoising Using Deep learning: Autoencoder Decoder Application 11

Satellite Image Denoising Using Deep Learning: Autoencoder D...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Pula, Rolando Advincula Cruz, Sean Chris Kirk Patrick P. Ilagan, Lorena College of Engineering Architecture and Technology University of Perpetual Help System DALTA Las Pinas Philippines

ISBN: (纸本)9798350355314

through deep learning Autoencoder Decoders, it is possible to clean noisy or damaged image data received from satellites. Two models with a PSNR of 25.6 dB and 25.54 dB were generated using a dataset consisting of 610 weather satellite images retrieved from the National Oceanic and Atmospheric Administration (NOAA) Weather (WX) satellites. these images were acquired via the ground station at the University of Perpetual Help System DALTA - Las Pinas campus. Initially, only a few alterations were made to the code in order to work with later Keras versions. However, this led to crashes during training as well as noticeable darkening in the predictions. the original code also used accuracy as the training metric, instead of PSNR. the rewrite rebuilt the model using updated Keras 3 API as well as introduced a PSNR metric. Doing so eliminated the darkening and crashes entirely. © 2024 IEEE.

关键词： Weather satellites

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Water Quality Classification Evaluation based on Water Quality Monitoring data 11

Water Quality Classification Evaluation based on Water Quali...

引用

11th international conference on Information Systems and Computing Technology, ISCTech 2023

作者： Yu, Ziwen Sheng, Yifu Li, Hao Li, Peilun Zhang, Jianjun Xiao, Ke Wang, Li Lin, Haijun Hunan Normal University College of Engineering and Design Hunan Changsha China China Mobile Communications Group Hunan Co. Ltd. Changsha Branch Hunan Changsha China Hunan Institute of Metrology and Test Hunan Changsha China

ISBN: (纸本)9798350342406

Water quality evaluation is an important foundation for the development and utilization of water resources. With the continuous application of artificial intelligence in production and life, water quality evaluation based on artificial neural network has gradually become a research hotspot. Since water quality monitoring data is only data, does not have category attributes, and cannot be directly used as input to neural networks, the water quality data must be firstly classified. In this paper, by constructing a large data set for water quality monitoring, the single-factor index method, principal component analysis method and fuzzy comprehensive evaluation method were used to label the water quality monitoring data by category. the labelling effects of these methods were compared and analysed, and the optimal category labelling scheme was selected to ensure the scientific accuracy of the later neural network water quality evaluation. © 2023 IEEE.

关键词： Principal component analysis

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Screening of Syndrome Elements and Construction of Diagnostic Model of Hyperactivity of Liver-Yang Syndrome Based on Machine learning 11th

Screening of Syndrome Elements and Construction of Diagnosti...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Hu, Sen Li, Zhao-Bing Sun, Yong-Kang Cui, Sin-Yue Xu, Fang-Biao Henan Provincial People’s Hospital Zhengzhou University People’s Hospital Henan Zhengzhou450000 China Henan University of Chinese Medicine Henan Zhengzhou450000 China

ISBN: (纸本)9789819721153

Objective: Based on a variety of machine learning algorithms, screen the hyperactivity syndrome e elements of liver yang syndrome. Methods: Using the classification of hyperactivity of liver yang syndrome standards and searching for relevant standards of hyperactivity of liver yang syndrome, the study constructed a matrix data set containing the syndrome elements of hyperactivity of liver yang syndrome and screened out key syndrome elements with the help of three machine learning algorithms, including XGBoost, SVM, and RF. Based on the results of the expert questionnaire, the key syndrome elements of liver yang syndrome are derived. Results: A total of 10 key elements were screened out through 3 machine learning algorithms. Combined with the results of multi region, multi field and multi-level research, the core syndrome elements of irritability, soreness and softness of the waist and knees, stringy pulse and red tongue were obtained. Conclusion: there is still a need to expand clinical samples and continuously optimize the diagnosis model of hyperactivity of liver-Yang syndrome based on expert opinions because of this study, as it provides a data base for the digitalization and objectification of TCM syndrome differentiation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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SecuSCADA: Building Secure SCADA Network with Obfuscated Malware Detection Technique 11

SecuSCADA: Building Secure SCADA Network with Obfuscated Mal...

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Kumar, Atul Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350348422

Supervisory Control and data Acquisition system are used in various industries for monitoring and controlling industrial processes and infrastructure. they are essential for keeping these critical systems running smoothly and safely collect and analyze real-time data from various sensors, machines, and other devices to monitor and control the processes. An attack on a SCADA system can have significant and far-reaching consequences for the organization responsible for the system, as well as for public safety and the environment. It is critical for organizations to take proactive steps to protect SCADA systems from attacks, including implementing security features including network segmentation, access limits, intrusion detection, and periodic vulnerability assessments and penetration testing. In this research paper, a model is proposed to build a secured SCADA network by detecting Obfuscated malware using machine learning algorithms. the SCADA server is shielded using a honeypot in the proposed approach, the honeypot machine can detect Obfuscated malware. the proposed approach experiments with a Decision tree classifier and XGBoost algorithm for detecting such malware in the SCADA network. the XGBoost machine learning technique outperforms in comparison with the decision tree classifier and is capable of more accurately detecting this hidden malware. © 2023 IEEE.

关键词： Network security

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741243

the proceedings contain 157 papers. the special focus in this conference is on Innovative Computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated learning and Programming learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge Computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis Technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in automated Mechanical Design.

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Multiclass Predictor and Diagnoses of Breast Cancer Using Machine learning 12

Multiclass Predictor and Diagnoses of Breast Cancer Using Ma...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Chauhan, Nupa Ram Sengar, Alok Singh Shukla, Ratnesh Kumar Kumar, Saurabh Teerthanker Mahaveer University Computer Science & Engineering UP Moradabad India Noida International University Computer Science & Engineering UP Greater Noida India Shambhunath Institute of Engg. & Technology Computer Science & Engineering UP Prayagraj India

ISBN: (纸本)9798350369861

Metastatic Breast Cancer is the most prevalent and life-threatening diseases worldwide, affecting millions of women and men each year. Among all the most important factors that significantly influence the diagnosis and treatment an early stages are vital for reducing the fatality rate in the fight against it. Breast cancer subtype classification is a multi-class classification problem that can be handled using computational methods. there are numbers of techniques as thermography, ultra-sound, mammography and magnetic resonance imaging (MRI) for the detection of cancer tumor. By combining these with machine learning techniques and automated diagnostic techniques, the potential for achieving highly accurate diagnoses with minimal false outcome. In this paper a novel intelligent integrated diagnosis system is proposed using classification models including Decision Tree Classifier, Naive Bayes Multinomial classifier, Support Vector Machines, K-Nearest Neighbor, XGBoost and Random forest using features selected at different threshold levels. the integration of machine learning techniques with clinical and histopathological data offers a powerful approach to analyzing complex patterns and relationships, thereby enhancing the ability to identify patients at high risk of metastasis in a timely manner. the performance and results evaluation shown an accuracy that varies for the models amongst five proposed approaches gives 94% to 99%, with a precision value of 95% to 98%, sensitivity from 96% to 99 %, and with specificity from 93% to 97%. this work will always support to the patient and doctors to take help of this model to predict the complexity of the breast cancer and its type to reduce the risk of this life threatening diseases. © 2023 IEEE.

关键词： Support vector machines

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10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

10th International Conference on Innovation and New Trends i...

引用

10th international conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th international conference on Innovation and New Trends in Information Technology, INTIS 2023

ISBN: (纸本)9783031473654

the proceedings contain 15 papers. the special focus in this conference is on Innovation and New Trends in Information Technology. the topics include: intelligent Multi-agent Distributed System for Improving and Secure Travel Procedures: Al-Karama-King Hussein Bridge Study Case;a New Approach for the Analysis of Resistance to Change in the Digital Transformation Context;augmented data Warehouses for Value Capture;a Parallel Processing Architecture for Querying Distributed and Heterogeneous data Sources;comprehensive data Life Cycle Security in Cloud Computing: Current Mastery and Major Challenges;systematic Mapping Study on Applications of Deep learning in Stock Market Prediction;exploring the Knowledge Distillation;intelligent Traffic Congestion and Collision Avoidance Using Multi-agent System Based on Reinforcement learning;BERT for Arabic NLP Applications: Pretraining and Finetuning MSA and Arabic Dialects;VacDist MAS for Covid-19 Vaccination Distribution: Palestine as a Case Study;Smart E-Waste Management System Utilizing IoT and DL Approaches;towards a Platform for Higher Education in Virtual Reality of engineering Sciences;Robust intelligent Control for Two Links Robot Based ACO Technique.

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Enhancing FinRL Trading Agents with Advance LLM-Processed Financial News: An Improved Approach Using DeepSeek-V3

Enhancing FinRL Trading Agents with Advance LLM-Processed Fi...

引用

intelligent data and Security (IDS), IEEE international conference on

作者： Satish Chandra G. Balakrishna School of Engineering Anurag University Hyderabad India

ISBN: (数字)9798331596613

ISBN: (纸本)9798331596620

the use of AI techniques to improve stock market related operations has been benefiting traders in the stock market. FinRL is a framework that has been shown to be effective in building Reinforcement learning (RL) based Trading Agents. Use of LLMs for performance improvement of the Agents is one of the recent initiatives. LLMs are being used to generate signals that could be combined with historical stock prices for better prediction of stock price movement trends leading to profitable trading. this paper aims to develop efficiently engineered prompts to generate DeepSeek-V3-based sentiment signals, integrate them into the FinRL environment, and improve the performance of reinforcement learning trading agents. the code, data, and trading agents are at: https://***/SatishChandraPhD/FinRL2025

关键词： Codes Large language models Buildings Reinforcement learning Market research Security Stock markets

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7th international conference on Innovative Computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative Computing, IC 2024

ISBN: (纸本)9789819741205

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