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

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Offline Policy Evaluation and optimization under Confounding 27

Offline Policy Evaluation and Optimization under Confounding

引用

27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Kausik, Chinmaya Makar, Maggie Lu, Yangyi Wang, Yixin Tan, Kevin Tewari, Ambuj Univ Michigan Ann Arbor MI 48109 USA Pinterest San Francisco CA USA Univ Penn Philadelphia PA 19104 USA

Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning. Using conventional methods for offline RL in the presence of confounding can not only lead to poor decisions and poor policies, but also have disastrous effects in critical applications such as healthcare and education. We map out the landscape of offline policy evaluation for confounded MDPs, distinguishing assumptions on confounding based on whether they are memoryless and on their effect on the data-collection policies. We characterize settings where consistent value estimates are provably not achievable, and provide algorithms with guarantees to instead estimate lower bounds on the value. When consistent estimates are achievable, we provide algorithms for value estimation with sample complexity guarantees. We also present new algorithms for offline policy improvement and prove local convergence guarantees. Finally, we experimentally evaluate our algorithms on both a gridworld environment and a simulated healthcare setting of managing sepsis patients. In gridworld, our model-based method provides tighter lower bounds than existing methods, while in the sepsis simulator, our methods significantly outperform confounder-oblivious benchmarks.

关键词： Health care

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Real-Time Diabetes Detection Using Machine learning and Apache Spark 4

Real-Time Diabetes Detection Using Machine Learning and Apac...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Saeed, Mohammed A. Saeed, Mogeeb A. Taiz Univ Dept Al Saeed Comp Networks & Distributed Syst Fac Engn & IT Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

the convergence of contemporary and cuttingedge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. this work presents distinct approaches and contributions, addressing specific challenges in healthcare and disease and diabetes analytics. the real-time healthcare for disease diabetes and the integration of big data analytics, machine learning, and real-time processing have paved the way for innovative solutions to address disease diabetes prediction and monitoring. It's explored innovative solutions to overcome challenges in healthcare analytics, offering real-time predictions and continuous monitoring for improved patient care. the realtime Healthcare-Diabetes dataset was analyzed using various machine learning models, and processing in real-time has led to innovative solutions to address diabetes prediction and monitoring. the Gradient Boosted Tree Classifier emerged as the most accurate model with an accuracy of 90.14%, followed by the Decision Tree Classifier at 84.62%, the Random Forest Classifier at 82.84%, the Linear Support Vector Classifier at 78.70%, and Logistic Regression at 64.69%. these results demonstrate the system's robustness and efficiency in real-time data collection, processing, and prediction. Leveraging Apache Spark and opensource big data technologies, specify data challenges and advocate for scalable, efficient, and cost-effective healthcare analytics. It contributes to the ongoing transformation of healthcare systems, demonstrating the effectiveness of advanced technologies in enhancing disease prediction, monitoring, and overall healthcare services.

关键词： Healthcare analytics real-time prediction Apache Spark machine learning distributed computing personalized healthcare

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ARON: Adaptive Resource optimization Network for AI-Driven Business Management 4

ARON: Adaptive Resource Optimization Network for AI-Driven B...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Verma, Pranay Singh, Gurinder Chaudhary, Naina thakur, Ayush Gupta, Astha Kler, Rajneesh Amity International Business School Amity University Uttar Pradesh Noida India Amity University Tashkent Uzbekistan Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350387490

this paper introduces the Adaptive Resource optimization Network (ARON), a novel AI-driven framework for strategic resource allocation and risk management in enterprise environments. ARON integrates deep reinforcement learning, natural language processing, and adaptive learning mechanisms to dynamically adjust resource allocation strategies in real-time, responding to market fluctuations and internal organizational changes. We present the architecture and methodology of ARON, including its data integration module, deep learning core, and natural language interface. An empirical evaluation across three diverse industry sectors-manufacturing, financial services, and e-commerce-demonstrates ARON's superior performance compared to traditional methods and static AI systems. Results show significant improvements in overall performance (up to 28.5%), resource allocation efficiency (13.4% to 40% increase), and risk management (37.2% reduction in Value at Risk). the study highlights ARON's enhanced adaptability to market volatility and its potential to transform business decision-making processes. We discuss the implications of these findings, acknowledge limitations, and propose directions for future research in AIdriven business management. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Prediction Model of Cardiovascular Diseases of Elderly Based on Support Vector Machine Combined with Particle Swarm optimization 4

A Prediction Model of Cardiovascular Diseases of Elderly Bas...

引用

4th IEEE international conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Yu, Shangmin Zhang, Shuoyang Chen, Peijiang School of Mechanical and Vehicle Engineering Linyi University Linyi China

ISBN: (纸本)9798350360721

Cardiovascular diseases (CVD) are common for elderly patients. With CVDs, patients often suffer greatly. the incubation period of CVD is generally long. thus, before the onset of the disease, it is necessary to test the risk of the disease and detect it in time for corresponding treatment. this effectively reduces the impact of CVD on elderly patients. Considering the diversity and complexity of the clinical features of CVD, we used the support vector machine (SVM) and particle swarm optimization (PSO) for machine learning. Experimental verification showed that this method helped the elderly analyze the risk probability of CVD and reduce its harm. © 2024 IEEE.

关键词： Support vector machines

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Cataract Detection and Classification Using YOLOv9 and Machine learning 4

Cataract Detection and Classification Using YOLOv9 and Machi...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Suryawanshi, Falguni Reddy, Ktv Department of Computer Science and Design Wardha India

ISBN: (纸本)9798331523923

Cataracts, a common age-related eye condition characterized by the clouding of the eye's natural lens, can impair vision if left untreated. Advances in cataract surgery have significantly improved treatment outcomes, but complications like posterior capsule rupture (PCR) still pose risks. Early detection is crucial, and recent developments in machine learning, particularly the YOLOv9 deep learning model, have shown promise in automating cataract detection using medical images such as eye scans. YOLOv9 can be trained to identify cataracts by recognizing distinct features like lens cloudiness and opacities. By leveraging image classification techniques and data augmentation, YOLOv9 achieves high accuracy in real-time cataract detection, assisting healthcare professionals in timely diagnoses and improving clinical workflows. Additionally, this model supports telemedicine applications by enabling remote analysis, expanding access to ophthalmic care. this paper highlights the significance of automated cataract detection using YOLOv9, its impact on early diagnosis, and its potential to enhance patient outcomes in ophthalmology through efficient image classification systems. © 2025 IEEE.

关键词： Ophthalmology

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NLP-Powered Data-Driven Career Decision Methodology Based on Machine learning 4

NLP-Powered Data-Driven Career Decision Methodology Based on...

引用

4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Jadhav, Ranjana Khanvilkar, Omkar Kharat, Janvi Katkar, Maitrey Kolate, Gita Kolhe, Babusha Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350385793

A chatbot application is intended to offer individualized career counseling. It examines the user's interests, educational history, and career objectives using sophisticated algorithms and machine learning. the app provides regular updates, tools, help with courses and exams, and individualized career insights. It also offers study guides and mock exams to help with exams. the user-friendly design of the chatbot makes navigating around it simple. Ongoing improvement is generated by system performance assessments and user input. By giving people the tools they need to make educated decisions about their educational and professional goals, the initiative seeks to transform career counseling. © 2024 IEEE.

关键词： Adversarial machine learning

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A flight slots allocation method for single airport based on Q-learning algorithm 4

A flight slots allocation method for single airport based on...

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2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Fang, Xinyi Chen, Haiyan Qian, Yuhang Yang, Lei State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510686304

the air traffic demand surpasses the capacity of most of the busiest airports worldwide. the mismatch between airport capacity and air traffic demand leads to serious congestion and delay problems. Since airport capacity is difficult to expand in a short time, in order to reduce flight delays, we need to adjust and optimize flight slots for flight applications, and allocate flight time resources scientifically and efficiently under the premise of meeting airspace resource constraints such as airport capacity. In this paper, we proposed an airport flight slots allocation method based on the Q-learning algorithm in reinforcement learning. Firstly, we preprocess the original data set. Data cleaning processes data with missing attribute values and removes irrelevant attributes. through data transformation, the data of string attribute is transformed into discrete numbers, and the time attribute is processed. After that, we construct a mathematical model of single-airport flight time allocation, which is a combinatorial optimization problem and determines the optimization objectives and constraints. then we apply the reinforcement learning algorithm Q-learning to solve this optimization problem, and the algorithm updates the strategy through interaction with the environment to get a better solution. Finally, we take Guangzhou Baiyun international Airport as a study case to train and verify the proposed single-airport flight time allocation model. the experimental results show that, the optimized flight slots satisfy the airport capacity constraints while minimizing the adjustment of flight schedules as much as possible, and adjust the flights at some times to alleviate the congestion. © 2025 SPIE ·

关键词： Combinatorial optimization

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Evaluation of Federated learning Strategies on Industrial Time Series Fault Classification 4th

Evaluation of Federated Learning Strategies on Industrial ...

引用

4th international conference on Advances in Distributed Computing and Machine learning, ICADCML 2023

作者： Prathap Kumar, Baratam Chouksey, Sameer Amarlingam, Madapu Ashok, S. ABB Global Industries and Services Private Limited Bengaluru India Depatment of Electrical Engineering National Institute of Technology Calicut Kerala India

ISBN: (纸本)9789819912025

Federated learning (FL) can enable industrial devices to collaboratively learn a shared Machine learning (ML) model while keeping all the training data on the device itself. the FL inherently solves the data privacy issues where as it also reduces the cloud data hosting cost and communication overhead. However, existing FL algorithms and frameworks are generically designed and not evaluated against the industrial applications. In case of industrial applications, the data generated from assets and machines are mostly non-independent and Identically Distributed (non-IID) which demands for an investigation and evaluation on the existing FL algorithms against such non-IID industrial data. this paper presents a survey of FL frameworks, algorithms, and optimizers, and evaluates their performance against the industrial data. the paper shares the results of extensive numerical experiments conducted with the edge device, Raspberry pi. the data set used in this study is benchmarking fault classification industrial data, called PRONTO data set, for both IID and non-IID cases. With the experimental results, the paper demonstrates that the adaptive optimization-based FL algorithm, FedAdam outperforms other algorithms on an industrial time series fault classification application. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time series

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Temperature Forecasting in Morocco Using Machine learning: optimization for Solar Energy applications 4th

Temperature Forecasting in Morocco Using Machine Learning: O...

引用

4th international conference on Digital Technologies and applications (ICDTA)

作者： Benayad, Mohamed Rochd, Abdelilah Houran, Nouriddine Simou, Mohamed Rabii Rhinane, Hassan Hassan II Univ Fac Sci Chock Geosci Lab Casablanca Morocco Green Energy Pk GEP Reg Rd Kelaa Km 3R206 Benguerir Morocco Hassan II Univ Casablanca Elect Engn & Intelligent Syst Lab ENSET Mohammedia Casablanca Morocco Sidi Mohamed Ben Abdellah Univ Innovat Technol Lab ENSA Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

In the current context of an energy transition, solar potential is an invaluable resource for producing renewable energy in Morocco. However, the efficiency for installation of solar panels requires accurate prediction of temperature. this paper purports to research new information about Geographic Information Systems coupled with Machine learning techniques for temperature forecasting in Morocco. In this work, we compare two models: the Random Forest (RF) one with the XGBoost one, based on a set of factors: PVOUT, GIT, OPTA, GHI, DNI, DIF, and DEM. Our results present a promising outlook for optimizing the solar panel installation process, using the value that a pixel has as a target for our prediction. Initial results indicate that the RF model has some promising levels of precision up to a level of 0.9971 R2, whereas XGBoost reached 0.977775. these results give good insights into the optimization for the solar panel installation at the pixel level for our purpose of predictions.

关键词： GIS IA RF XGboost Temperature Solar Energy Forecasting

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Enhancing College Education: An AI-driven Adaptive learning Platform (ALP) for Customized Course Experiences 9

Enhancing College Education: An AI-driven Adaptive Learning ...

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9th international conference on optimization and applications, ICOA 2023

作者： Altaleb, Hanan Mouti, Samar Beegom, Shajeela Khawarizmi International College Information Technology Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350312546

the main aim of this paper is to design an Adaptive learning Platform (ALP) based on Artificial intelligence (AI) algorithm for students taking college courses. In this platform, course content and methods are customized to meet the needs of individual students based on AI algorithms. ALP aims to provide efficient, effective, and customized course learning paths based on student level. this platform enhances student performance in the course learning outcome by adjusting the instructional content, pace, and delivery methods to the learner's individual needs and preferences. ALP uses Reinforcement learning (RL) algorithm and data analytics techniques to analyze the student's learning behaviors, strengths, and weaknesses, and provide personalized recommendations on how to improve their learning outcomes. © 2023 IEEE.

关键词： Reinforcement learning

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