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

共 7263 条记录，以下是1321-1330 订阅

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

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

引用

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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Multiclass Predictor and Diagnoses of Breast Cancer Using Machine learning 12

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

引用

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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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

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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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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Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray using Deep Attention Mechanism 11

Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray usin...

引用

11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Zaman, Muhammad Fatima, Tahseen Hameed, Sana Haider, Shahzeb Akhunzada, Adnan Azeem, Muhammad University of Lahore Faculty of Computer Science Sargodha Pakistan University of Saint Thomas School of Engineering Saint PaulMN United States University of Doha for Science and Technology Department of Data and Cybersecurity Doha24449 Qatar

ISBN: (纸本)9798350367300

Development of deep learning technologies has resulted in a major revolution in the field of medical diagnostics in recent years. the purpose of this research is to examine the usage of Convolutional Neural Networks (CNNs) in combination with an integrated attention mechanism known as CoATNet to improve the accuracy and responsiveness of paediatric pneumonia diagnosis using chest X-ray analysis. the major goal of this research project is to build and verify a powerful deep learning model capable of detecting mild pneumonia symptoms in children's chest X-rays. CoATNet's innovative attention approach allows it to provide more weight to areas deemed to be extremely important. this is in addition to its ability to recognise major patterns in images. Furthermore, the paper considers the issues posed by class imbalances in medical imaging datasets and undertakes an in-depth assessment of how effectively CoATNet works across a large dataset of paediatric chest X-rays. CoATNet's remarkable performance was shown by its ability to achieve a Micro Average of 98.2%, a Weighted Average of 98.7%, and an Accuracy of 99.7%. these measures show that the model can distinguish between instances of pneumonia and those do not have the illness, and that it is resilient when faced with class imbalances. F1 Score of 98.9% reflects a well-balanced trade-off between Precision 98.7% and Recall 98.2%. the integration of deep learning technologies with attention processes in CoATNet has resulted in a big step forward in the development of diagnostic models that are more visible and useful. © 2024 IEEE.

关键词： Pediatrics

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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)9789819741816

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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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Introducing Task-Adaptive Loss to Multitask learning for Electronic Healthcare Prediction 11

Introducing Task-Adaptive Loss to Multitask Learning for Ele...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Tsien, Edward Wu, Dezhi Tong, Yan Lopez-De Fede, Ana Gareau, Sarah University of South Carolina College of Engineering and Computing ColumbiaSC United States University of South Carolina Institute for Families in Society ColumbiaSC United States

ISBN: (纸本)9798350302639

Multi-Task learning (MTL) is a machine learning paradigm that allows for a model to simultaneously train and predict multiple related tasks/targets using a shared core network and task-specific branching layers. the related nature of the tasks can be exploited to regularize the model and has been shown to increase performance in all tasks. Medical healthcare records (MHRs) taken on events such as pregnancy often track multiple related targets such as maternal morbidity factors and long-term health outcomes which serve as an excellent testbed for MTL models. Imbalance in tasks leads to MTL models being dominated by a few tasks as well as struggling to progress during training due to conflicting loss function parameters, resulting in generalized but unspecific models. this study introduces Task-Adaptive Loss weighting, a novel method to address imbalanced tasks and conflicting loss and demonstrates iton a prenatal MHR dataset to create a prediction tool for adverse maternal outcomes. © 2023 IEEE.

关键词： Deep learning

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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...

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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

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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