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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： Deep reinforcement learning

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st international conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Cloud Process Optimization: A Hybrid GA-PSO Methodology

Cloud Process Optimization: A Hybrid GA-PSO Methodology

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2023 international conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Divya Rani, M.S. Savitha, R. Naval, Preeti Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Within the realm of computing in clouds, optimising cloud operations is a major problem, especially when it comes to striking a balance between resource utilisation and effective completion of tasks that is regulated by complex mutual dependence. In the study article "clouds Service Optimisation: A Hybrid GA-PSO The technique,"a combination of approaches to efficient task assignment to services is presented. This approach is designed to reduce runtimes, reduce expenses in cloud settings, and reorganise the allocation of the load of interrelated duties across various resources. The hybrid technique that combines PSO and GA shows impressive performance in terms of shorter completion times and lower costs for execution in comparison to other current technologies. It is noteworthy because it greatly maximises the distribution of tasks across available resources, resulting in an important rise in resource utilisation. Experimental results empirically validate the effectiveness of the GAPSO algorithms and demonstrate its advantage over earlier computer approaches. Compared to traditional approaches, the algorithm shows greater convergence to optimum outcomes while retaining superior quality. This highlights the hybrid approach's effectiveness and productivity and establishes it as a strong contender for resolving the internet of things paradigm problems with work distribution, resource optimisation, and cost containment. © 2023 IEEE.

关键词： Cloud computing Cost optimization Genetic algorithms Hybrid GA-PSO method Load balancing Make span reduction Multi objective optimization Performance evaluation VM (Virtual Machine) allocation Workflow management

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Employing Deep Learning Algorithms for Real-Time Analysis and Prediction of Financial Markets and Investment Strategies 13

Employing Deep Learning Algorithms for Real-Time Analysis an...

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rajendran, Naveen Kumar Kumari, Sweta Pandey, Vijay Kumar Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India Atlas SkillTech University Department of Isme Maharashtra Mumbai India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350305463

A new age of data-driven decision-making has begun with the incorporation of deep learning algorithms into the domain of financial markets and investment strategies. A summary of our thorough investigation of this groundbreaking convergence is given in this abstract, which covers all the important points from the introduction to the techniques used, the outcomes attained, and the final observations. The introduction lays the groundwork for the paradigm change in finance, which has enabled real-time analysis and prediction via the use of deep learning models. The complexities of financial markets, characterized by high-dimensional time series data and non-linearities, have traditionally presented difficulties for conventional statistical techniques. With the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), it is now possible to anticipate stock prices, exchange rates, and other financial variables with greater accuracy. In comparison tests, the Moving Average convergence Divergence (MACD) approach yielded a Sharpe ratio of 1.8, whereas LSTM showed a ratio of 2.5. In a similar vein, the Transformer model outperformed the Moving Average (MA) technique, with a Sharpe ratio of 3.0 against 1.5. The main conclusions from this investigation are summarized in the conclusion. Although there are still issues with computing needs and model interpretability, deep learning in banking has the potential to revolutionize the industry. Deep learning algorithms are redefining investing techniques and enabling financial professionals to better traverse the complexity of the financial world. © 2024 IEEE.

关键词： Forecasting

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Design and Implementation of A Novel Eye Fatigue Analysis System for Safe Field Workers 8

Design and Implementation of A Novel Eye Fatigue Analysis Sy...

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8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Lee, Jieun Kim, Namji Park, Jihyeon Kim, Tae-Yong Park, Jiwan Jeong, Jongpil Sungkyunkwan University Dept. of Smart Factory Convergence Suwon Korea Republic of Sungkyunkwan University Dept. of Applied Artificial Intelligence Suwon Korea Republic of

ISBN: (纸本)9798350373615

This paper proposes a study to analyze worker fatigue and prevent accidents in a smart factory environment. This study utilizes MPE blink (Multi-Person Eyeblink) dataset and InstBlink algorithm to detect the number of eye blinks of workers in real-Time and proposes a method to improve the accuracy of the algorithm to measure worker fatigue more accurately. This research plays an important role in improving worker safety and productivity. In manufacturing environments, workers spend long hours working on monitors, often unable to look away from the screen. This can lead to poor eye health and increased fatigue. The system estimates the fatigue level by detecting the worker's eye blinks before work and displays a warning screen to prohibit physical labor when the number of blinks is detected below 15 per minute. This helps to protect the safety of workers and prevent accidents in the work environment. © 2023 IEEE.

关键词： Computer vision

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An Ensemble of PSO and Artificial Electric Field Algorithm for Computationally Expensive Optimization Problems 12th

An Ensemble of PSO and Artificial Electric Field Algorithm...

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12th international conference on Soft computing for Problem Solving, SocProS 2023

作者： Chauhan, Dikshit Yadav, Anupam Department of Mathematics Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144027 India

ISBN: (纸本)9789819731794

Population-based optimization algorithms are extensively studied to address various types of optimization problems. Among them, the artificial electric field algorithm (AEFA) has gained popularity. However, AEFA still has limitations such as slow convergence speed, insufficient memory utilization, and limited search capabilities. To overcome these challenges, we propose a hybrid algorithm called particle swarm algorithm (PSO) and artificial electric field (PSAEF) that introduces a novel definition of Coulomb’s constant. The new definition of Coulomb’s constant in PSAEF enhances its exploration rate, enabling it to avoid local optima. Additionally, the algorithm incorporates knowledge of the global best solution to improve its exploitation phase and convergence rate. We evaluate the performance of PSAEF on a set of bound-constrained IEEE CEC benchmarks and compare the results with 11 state-of-the-art optimization algorithms. Extensive analyses and statistical testing, including the Wilcoxon signed-rank test, are conducted to validate the results. The experimental findings demonstrate that PSAEF outperforms other state-of-the-art algorithms in terms of accuracy and statistical significance. It achieves superior performance on 92 and 80% (average percentile) of the problems, respectively. The results indicate that the proposed hybrid algorithm exhibits enhanced search capabilities and faster convergence rates compared to other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Swarm intelligence

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An IoT-based real-Time sensing model for rainfall-runoff using big data analytics 3

An IoT-based real-Time sensing model for rainfall-runoff usi...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Saravanan, T. Deepa, S. Sasikumar, P. Simum, Mohamed Saber Sharit, Haider GITAM School of Technology Department of CSE Bengaluru India Government College of Engineering Department of Physics Salem India Malla Reddy Institute of Engineering & Technology Department of CSE Secunderabad India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

Millions of dollars' worth of infrastructure has been destroyed as a result of floods' destructive effects in the past. It is a serious issue that leads to crop degradation, population decline, loss of infrastructures, and the deconstruction of many public utilities. An efficient strategy to handle this is to warn the neighborhood of an impending flood and give them plenty of time to leave and safeguard their property. There is still no global, universal system that can gather, store, evaluate huge information and produce flood prediction results despite extensive research. Using the convergence of big data and the Exponential Smoothing Technique (EST), forecasting architecture and a social collaborative IOT-based smart flood monitoring are suggested in this research. Based on flood events tracked by rainfall sensors in the catchment, we implemented an EST-based IoT network model to simulate the rainfall-runoff process. The findings demonstrate that, as opposed to physical-based and conceptual models, the suggested network is more appropriate for rainfall-runoff models. Data is retrieved from the open-source Kaggle meteorological database, and experimental evaluation is done. The results demonstrated the viability of the 23 suggested architectural designs. © 2023 IEEE.

关键词： Floods

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ACLPSO with Dynamic Interval Scaling and Learning Probability Tradeoff Coefficients 15

ACLPSO with Dynamic Interval Scaling and Learning Probabilit...

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15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Zhang, Yi Yu, Xiang Xu, Kaiwen Nanchang Institute of Technology School of Information Engineering Nanchang China Nanchang Institute of Technology School of Civil and Architectural Engineering Nanchang China

ISBN: (纸本)9798350308693

Adaptive comprehensive learning particle swarm optimization (ACLPSO) is a powerful particle swarm optimization variant. In ACLPSO, the interval scaling coefficient s and learning probability tradeoff coefficient v are key parameters dominating convergence. ACLPSO statically sets values for s and v. However, reported experimental results have shown that ACLPSO may require different value pairs of s and v on different benchmark functions to derive the global optimum or a near-optimum with high fitness accuracy, and there is no rule on determining the most appropriate value pair. To eliminate the need of manually tuning s and v, this paper proposes to dynamically set values for the two coefficients. Experimental results on various benchmark functions demonstrate that ACLPSO with dynamic s and v is able to find the global optimum or a near-optimum with high fitness accuracy on most functions, significantly better than ACLPSO with each static value pair of s and v. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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Privacy-Preserving Federated Learning for Brain MRI Analysis 6

Privacy-Preserving Federated Learning for Brain MRI Analysis

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6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Dcunha, Limark Dsouza, Larren Ramanan, Gokul Chaudhari, Sheetal Sardar Patel Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9798350394702

Advancements in medical imaging facilities have led to a substantial increase in the generation of medical imaging data on a daily basis. This creates an excellent opportunity for researchers to develop data-driven methods to improve healthcare. However, one of the major challenges in this regard is the requirement of large, diverse, and high-quality datasets for effective training, which are often scarce within a single institution. Although combining datasets from multiple institutions into a central repository would be ideal, data privacy regulations pose significant obstacles, particularly when involving institutions from different countries. To address this issue, Federated Learning (FL) can be employed, enabling the training of machine learning models on distributed data from various sites. In this research, federated learning has been employed for Brain MRI Analysis and to assess the generalizability of the models using data from institutions outside the federation. The distributed architecture of FL, however, introduces a vulnerability to adversarial users who may manipulate the global model by modifying their local updates or datasets. To overcome this challenge, FedByz aggregation algorithm has been proposed, which enhances the resilience of FL against Byzantine users. The experiments demonstrate that this approach achieves convergence even in the presence of Byzantine users, and its accuracy is comparable to that of traditional federated learning benchmarks. © 2023 IEEE.

关键词： Large datasets

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IoT-Assisted Intelligent Vehicle Tracking System using Cloud computing 13

IoT-Assisted Intelligent Vehicle Tracking System using Cloud...

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Nwankwo, Odinachi Udemezuo Nwakanma, Cosmas Ifeanyi Kim, Dong Seong Lee, Jae-Min It Convergence Engineering Kumoh National Institute of Technology Gumi Korea Republic of Ict Convergence Research Center Kumoh National Institute of Technology Gumi Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

This work presents a Track Intelligent Security System (TISS) vehicle location tracking in the event of vandalism or theft, hence, with the coordinates being sent to the owner through internet of things (IoT) cloud. The vehicle's current location can be traced thereby enabling vehicle recovery. The proposed scheme is designed using arduino uno, SIM808 module with GPS antenna, Relay module, power supply for the system. The IoT infrastructure used was Message Queue Telemetry Transport (MQTT), Adafruit IO as the cloud provided by Adafruit industries. The Global Positioning System (GPS) antenna measures the current coordinates (longitudes and latitudes) of the vehicle after it has been activated through the IoT cloud. The coordinates can then be viewed using Google Earth. The software design was done using C/C++ programming language and Arduino Integrated Development Environment. A novel feature is the ability to use a Adafruit IO cloud platform via a communication network to monitor position of a stolen vehicle and remotely stop such a vehicle from moving further with unlimited range of network coverage. This makes the device better than IoT implemented with WiFi which only covered 100m. © 2022 IEEE.

关键词： Internet of things

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