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A survey on model-based reinforcement learning

science China(Information sciences) 2024年第2期67卷 59-84页

作者： Fan-Ming LUO Tian XU Hang LAI Xiong-Hui CHEN Weinan ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir. ai Department of Computer Science and Engineering Shanghai Jiao Tong University

Reinforcement learning(RL) interacts with the environment to solve sequential decision-making problems via a trial-and-error approach. Errors are always undesirable in real-world applications, even though RL excels at playing complex video games that permit several trial-and-error attempts. To improve sample efficiency and thus reduce errors, model-based reinforcement learning(MBRL) is believed to be a promising direction, as it constructs environment models in which trial-and-errors can occur without incurring actual costs. In this survey, we investigate MBRL with a particular focus on the recent advancements in deep RL. There is a generalization error between the learned model of a non-tabular environment and the actual environment. Consequently, it is crucial to analyze the disparity between policy training in the environment model and that in the actual environment, guiding algorithm design for improved model learning, model utilization, and policy training. In addition, we discuss the recent developments of model-based techniques in other forms of RL, such as offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Furthermore,we discuss the applicability and benefits of MBRL for real-world tasks. Finally, this survey concludes with a discussion of the promising future development prospects for MBRL. We believe that MBRL has great unrealized potential and benefits in real-world applications, and we hope this survey will encourage additional research on MBRL.

关键词： reinforcement learning model-based reinforcement learning planning model learning model learning with reduced error model usage

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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An optimized pair-wise comparison approach for automated feature weight assignment in content-based image retrieval system

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Multimedia Tools and applications 2024年 1-31页

作者： Rout, Narendra Kumar Ahirwal, Mitul Kumar Atulkar, Mithilesh Department of Computer Application NIT CG Raipur492010 India Department of Computer Science and Engineering MANIT M.P Bhopal462003 India

A content-based image retrieval system (CBIR) needs intensity/weighted importance for individual features of an image to find similar images in the database to achieve better results. Generally, these weights are assigned manually and are difficult to calculate as per the nature of the images. An experienced user may be able to retrieve images with higher accuracy by providing suitable weights for features in comparison to that of an inexperienced user. There is a need to develop a method that is independent of manual assignment of weights. To fulfil that need, an optimization-based pair-wise comparison approach has been developed for reducing the user intervention in the retrieval system and finding the optimal feature weight based on the nature of a benchmark COREL 10kimage database. The proposed work is implemented with different optimization techniques to optimize the pair-wise comparison method known as the Analytic hierarchy process (AHP). For optimization, algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Grey-wolf optimization (GWO), and Jaya algorithm (JAYA) have been used to get optimal weight for image feature. As per the results and observations, the AHP-PSO approach performs promising accuracy in terms of average precision and recall as compared to distinct optimized AHP approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Deep Learning-Based Detection of Foliar Diseases in Apple Plants Using an Assembled CNN Model 3

Deep Learning-Based Detection of Foliar Diseases in Apple Pl...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Sahu, Yatendra Bhargava, Arpita Thakur, Ghanshyam Singh Jain, Rishi IIIT Department of Computer Science and Engineering Bhopal India MANIT Department of Computer Application Bhopal India

ISBN: (纸本)9798350391565

The apple industry faces significant economic losses due to diseases and pests, contributing to low productivity levels. Detecting apple diseases promptly is essential for controlling their spread and improving overall productivity. However, early identification and diagnosis of these diseases are challenging due to various factors. In this work, we evaluated the performance of three distinct models: a custom CNN, Xception, and Resnet152V2. These models were tested on an expert-annotated dataset of apple diseases, comprising approximately 1821 high-quality RGB images across different categories of following types: C1: scab affected apple, C2: cedar apple affected by rust, C3: diseases of complex nature, and C4 samples of healthy apples. The custom CNN based proposed model trained for 50 epochs could yield an approximate accuracy of around 95%. © 2024 IEEE.

关键词： Plant diseases

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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Exploring AI Innovations in Automated software Source Code Generation: Progress, Hurdles, and Future Paths

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Informatica (Slovenia) 2024年第8期48卷 125-136页

作者： Odeh, Ayman Odeh, Nada Software Engineering and Computer Science Department College of Engineering Al Ain University Al Jimi Al Ain United Arab Emirates

In today's dynamic world of software development, the demand for efficient and rapid creation of high-quality code has never been more pronounced. Automated software source code generation (ASSCG) emerges as a compelling solution to meet this demand, offering significant advantages in terms of speed, accuracy, and scalability. This paper aims to explore the critical role of automated software source code generation and its profound significance in modern software development practices. By navigating through the intersection of ASSCG, AI innovations, and the challenges therein, this paper endeavors to provide a comprehensive understanding of this transformative field and pave the way for informed decision-making and advancements in software development practices. This paper delves into the critical role of ASSCG and its transformative impact on modern software development. In this work, we endeavor to delve into the multifaceted landscape of automated code generation, assessing its significance, the transformative potential of AI innovations, and the challenges and objectives inherent in this evolving domain. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Deep learning

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Predictive Analytics for Parkinson's Disease Detection Leveraging Deep Learning Algorithms

Predictive Analytics for Parkinson's Disease Detection Lever...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Priyanka, S. Jahaan, R. Reshmi Thenmalar, T. Kongu Engineering College Department of Computer Application Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331543617

Parkinson disorder is a neurological disease that progresses gradually which is typified by the brain's dopamine-producing neurons becoming exhausted. This neuronal loss results by symptoms including tremors, muscle stiffness, bradykinesia, and balance difficulties. Early detection and accurate prediction of PD progression are especially crucial for improving management and intervention strategies. This study builds a prediction model with the use of MRI datasets. To detect Parkinson's disease from MRI data, this study uses deep learning algorithms, such as Inception, Xception, and VGG19, and evaluates how well they perform in terms of accuracy. © 2024 IEEE.

关键词： Neurons

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An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

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Recent Patents on engineering 2024年第2期18卷 88-104页

作者： Rath, Swarnalata Das, Nilima R. Pattanayak, Binod Kumar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be id

关键词： Financial markets

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