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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

引用

23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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StudentCoin Price Prediction and Relation to Blockchain Education 10

StudentCoin Price Prediction and Relation to Blockchain Educ...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Rashmeet Sonia Iorio, Carmela University of Naples Federico Ii Department of Economics Science and Statistics Naples Italy Yogananda School of AI Computer and Data Science Shoolini University Solan India

ISBN: (纸本)9789380544472

The newly introduced cryptocurrency called StudentCoin which is identified by the acronym STC is a blockchain-based project. It is the first platform in the world for easy tokenization. It allows the users to learn about cryptocurrencies, blockchain technology and their use cases. Users can create their own tokens, coins and also trade them on the same terminal. This paper uses easy-to-understand modeling and simulation techniques to make price predictions for blockchain education-oriented cryptocurrencies like StudentCoin. We have used the Monte Carlo simulation technique to determine if the price of StudentCoin is expected to increase within a month, and that combining education with blockchain and cryptocurrency platforms is beneficial. The study also aims to find out if college students are aware of cryptocurrencies and see if it's possible to combine education and cryptocurrency. The paper contributes to promoting awareness among university students about responsible investment practices in the cryptocurrency market. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision 2

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

引用

2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poongavanam, Elumalaivasan Prathik, A. Munirathinam, T. Kayalvizhi, V. Rajesh, R. Mahavishnu, C. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Shreenivasa Engineering College Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350328202

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases. © 2024 IEEE.

关键词： Real time systems

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HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs

HTCCN: Temporal Causal Convolutional Networks with Hawkes Pr...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Chen, Tingxuan Long, Jun Yang, Liu Wang, Zidong Wang, Yongheng Jin, Xiongnan School of Computer Science and Engineering Central South University Hunan China Big Data Institute Central South University Hunan China Zhejiang lab Zhejiang China

ISBN: (纸本)9798891761148

Temporal knowledge graphs (TKGs) serve as powerful tools for storing and modeling dynamic facts, holding immense potential in anticipating future facts. Since future facts are inherently unknowable, effectively modeling the intricate temporal structure of historical facts becomes paramount for accurate prediction. However, current models often rely heavily on fact recurrence or periodicity, leading to information loss due to prolonged evolutionary processes. Notably, the occurrence of one fact always influences the likelihood of another. To this end, we propose HTCCN, a novel Hawkes process-based temporal causal convolutional network designed for temporal reasoning under extrapolation settings. HTCCN employs a temporal causal convolutional network to model the historical interdependence of facts and leverages Hawkes to model link formation processes inductively in TKGs. Importantly, HTCCN introduces dual-level dynamics to comprehensively capture the temporal evolution of facts. Rigorous experimentation on four real-world datasets underscores the superior performance of HTCCN. ©2024 Association for Computational Linguistics.

关键词： Extrapolation

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Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis 25

Mining Evidence about Your Symptoms: Mitigating Availability...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zhang, Junti Zhu, Zicheng Li, Jingshu Lee, Yi-Chieh Institute of Data Science National University of Singapore Singapore Singapore School of Computing National University of Singapore Singapore Singapore Computer Science School of Computing National University of Singapore Singapore Singapore Computer Science National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713941

People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis. © 2025 Copyright held by the owner/author(s).

关键词： Diagnosis

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An Improved Algorithm of Detecting Abnormal data in Geological Disaster System 6

An Improved Algorithm of Detecting Abnormal Data in Geologic...

引用

6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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Improving the Accuracy of Iris Recognition Based on Attention Mechanism 6

Improving the Accuracy of Iris Recognition Based on Attentio...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Wang, Zike Wang, Jianguo Zhong, Lei Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

Iris recognition algorithms are becoming more and more widely used in daily life, and the accuracy requirements for iris recognition algorithms are getting higher and higher. Therefore, in this paper, an improved Shufflenetv2 algorithm is proposed as a way to improve the speed and accuracy of current iris recognition algorithms in daily life. In this paper, deep and shallow features are filtered by using the channel attention mechanism, which enables the model to acquire feature representations of both underlying and high-level semantics. In addition, in the loss function, this paper uses the focal loss function instead of the cross entropy loss function to achieve a solution to the imbalance between the number of positive and negative samples and prevent too much learning to negative sample features. By validating on the CASIA-Iris-Thousand dataset, which is commonly used in iris recognition tasks, the proposed model achieves an equal error rate (EER) of 1.66% compared to the Shufflenetv2 model, which has an EER of 5.3%, and optimizes by 3.64%. The model proposed in this paper can effectively improve the accuracy of iris recognition. © 2023 IEEE.

关键词： Convolutional neural networks

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Temporal Feature Mining in Dynamic Graph of Brain Connectivity data

Temporal Feature Mining in Dynamic Graph of Brain Connectivi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liu, Tao Jing, Ming Zhang, Guangwei Zhang, Li Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350337020

In recent years, the graph feature mining method of brain connection data based on graph theory has been regarded as a popular and universal technology in the field of neuroscience. How to mine valuable information from brain connection data has become a research hotspot. Current research shows that the pathogenic factors of attention deficit and hyperactivity disorder (ADHD) may be caused by the abnormal connection between brain network structures. In order to find out the pathogenic factors of ADHD patients, we also carried out frequent sub-graph mining on the connectivity graph data of brain functional network. By constantly adjusting the sup-port threshold, all the subgraphs of ADHD patients and healthy control group were mined, and the differences in brain region connectivity were successfully found out. By combining the recently introduced neural document embedding model with traditional pattern mining techniques, we regard the brain network connection structure graph as the document and frequent subgraph as the atomic unit of the embedding process. By learning the mapping, each graph can be mapped to a D-dimensional continuous vector. The mapping needs to capture the similarity between the graphs. Feature vectors can be used as the direct input of graph classification in many traditional machine learning methods. Finally, support vector machine in machine learning is used to verify the accuracy of classification, and the results show that the accuracy is high. © 2023 IEEE.

关键词： ADHD brain network identification fMRI Frequent subgraph mining Graph data mining

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