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Dynamic Jumps of an Agile Bicycle Through Reinforcement learning

Dynamic Jumps of an Agile Bicycle Through Reinforcement Lear...

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On-Line conference (ONCON), Industrial Electronics Society Annual

作者： Zhuochen Yuan Linqi Ye Houde Liu Zhang Chen Bin Liang Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Future Technology Shanghai University Shanghai China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

Controlling bicycles during dynamic maneuvers, such as jumps, poses significant challenges due to the intricate demands of balance and stability. This study utilizes the Proximal Policy optimization (PPO) algorithm to control both ground navigation and dynamic jumps. Stability on flat terrain is maintained through steering adjustments, while jumps are facilitated by vertical thrust. The proposed approach is validated through simulation, showing high success rates in jump execution, along with improved balance and adaptability. These results underscore the potential of reinforcement learning-based control systems in managing complex dynamic movements and contribute to the development of more robust motion control strategies for bicycles on both structured and unstructured terrains. The attached video is given in https:/***/video/***4.

关键词： Navigation Heuristic algorithms Simulation Dynamics Bicycles Reinforcement learning Stability analysis Real-time systems Timing optimization

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A Comprehensive Review on Different CNN Architectures

A Comprehensive Review on Different CNN Architectures

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Anuj Thakur Saroj Kr. Biswas Sounak Majumdar Dalton Meitei Thounaojam Department of CSE NIT Silchar Silchar India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Convolutional Neural Networks (CNNs) have rev- olutionized the field of computer vision, transforming tasks such as image classification, segmentation, and object detection with remarkable efficacy. This review provides a comprehensive overview of the evolution, architectures, and applications of CNNs. This paper begins with a historical perspective, tracing the development of CNNs from their inception to the present day. The paper discusses the fundamental components of CNN archi- tectures, including convolutional layers, pooling layers, activation functions, and normalization techniques. In addition, it explores the advances in network design, such as the introduction of deep and residual networks, and innovative strategies such as attention mechanisms. This paper also discusses the impact of transfer learning and pre-trained models on practical applications. The purpose of this paper is to offer a nuanced understanding of their capabilities and potential future directions by highlighting the strengths and limitations of current CNN architectures. In addition, this paper also addresses emerging trends and challenges in the field, offering insights into how CNNs can continue to evolve and adapt to new domains and applications.

关键词： Deep learning Technological innovation Image segmentation Computer vision Reviews Computational modeling Computer architecture Object detection Convolutional neural networks Image classification

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iTextbooks 2021 - Proceedings of the 3rd International Workshop on intelligent Textbooks 2021, Co-located with 22nd International conference on Artificial Intelligence in Education, AIED 2021

iTextbooks 2021 - Proceedings of the 3rd International Works...

引用

3rd International Workshop on intelligent Textbooks, iTextbooks 2021

The proceedings contain 13 papers. The topics discussed include: intelligent textbooks;mathematical formula representation via tree embeddings;dental TutorBot: exploitation of dental textbooks for automated learning;transforming textbooks into learning by doing environments: an evaluation of textbook-based automatic question generation;the mobile fact and concept textbook system (MoFaCTS) computational model and scheduling system;using semantics of textbook highlights to predict student comprehension and knowledge retention;contextual definition generation;generation of assessment questions from textbooks enriched with knowledge models;integrating textbooks with smart interactive content for learning programming;and PUMPT: an e-textbook platform based on a personal user model for learning.

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Prediction Of Groundwater Level Using Advance Machine learning Techniques

Prediction Of Groundwater Level Using Advance Machine Learni...

引用

intelligent Technologies (CONIT), International conference on

作者： Vivek Tiwari Manikant Verma Indian Institute of Technology Roorkee Uttarakhand India National Institute of Technology Raipur Chhattisgarh India

Climate change is a highly debated topic today, with various organizations conducting initiatives to understand its impact. The Intergovernmental Panel on Climate Change (IPCC) is recognized for publishing authoritative reports on climate change and its effects. In Chhattisgarh, a state where groundwater is the primary source of irrigation, the declining groundwater level is a significant concern. This study employs advanced prediction methods using Machine learning (ML) algorithms to address this issue. Rainfall and temperature data are utilized for training various models, such as Random Forest (RF), K Nearest Neighbor (KNN), Artificial Neural Network (ANN), and Support Vector Machine (SVR). We utilized the Regional Climatic Models (RCMs) to determine the potential impact of climate change. We conducted a study using 38 rain gauge stations, obtaining observed data from government organizations and simulated data from RCMs. Two bias correction methods were performed: power transformation (PT) and quantile mapping (QM). The most suitable circulation model was selected based on regression and Nash Sutcliff efficiency coefficients. Time series analysis was then performed to determine trends. Results showed that power transformation performed better than quantile mapping after bias correction and was therefore used for further *** all the ML models, the Random forest model gave the best results with minimum root mean square error, so the random forest was selected for predicting the future trajectory of groundwater.

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Aspect-Based Sentiment Analysis on Movie Reviews using Machine learning

Aspect-Based Sentiment Analysis on Movie Reviews using Machi...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Riju Ray Srijoni Dey Debalina Bagchi Gargee Das Pratibha Dutta Anjan Kumar Payra Dept of CSE(AI&ML) Institute of Engineering and Management Newtown Kolkata India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

A thorough method for Aspect-Based Sentiment Analysis (ABSA) of movie reviews is presented in this study. The goal of the study is to extract sentiments from reviews that are specific to elements like acting, plot, etc. Machine learning models like Multinomial Naïve Bayes, Logistic Regression, K-Nearest Neighbors (KNN), Linear SVC, and XGBoost were used to classify sentiment. The best results were obtained by developing an ensemble model that combined Naïve Bayes, Logistic Regression, and Linear SVC to increase accuracy. With an accuracy of 89.67%, the results show that the ensemble performed better than individual models during the validation and test phases. The results demonstrate how ensemble learning can enhance sentiment classification tasks.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Static VAr compensators Nearest neighbor methods Motion pictures Bayes methods Ensemble learning Reliability

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Data-Driven Approaches for Personalized Sleep Disorder Diagnosis

Data-Driven Approaches for Personalized Sleep Disorder Diagn...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： R. Asmita T.H. Hrithik K. H. Akhil Nisha Mishra V. S. Kirthika Devi Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

These days, sleeping disorders are a concern for young people and people with a variety of health issues. It is important to keep in mind that sleep problems are not a recent occurrence and should not be sought after or accepted as a trend, even though it is true that issues related to sleep have garnered more attention and discussion in recent years due to growing knowledge and research. Numerous problems, such as anxiety, depression, and mental health strain, can be brought on by this illness. Machine learning (ML) can handle a wide range of tasks, including classification, prediction, and decision-making. To analyze and identify sleeping disorders using ML and can also be employed to gauge a person's level of stress. This paper presents the DL models and ML algorithms that have been deployed to sleep stage classification in the literature to date. The effectiveness of Adaboost models is investigated in this work. These models' research findings suggest the ideal parameter combination for improved performance.

关键词： Support vector machines Accuracy Machine learning algorithms Search methods Prediction algorithms Classification algorithms Bayes methods Tuning Stress Strain

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A Fusion of CNN and Grey Wolf optimization-Enhanced BiGRU for Epileptic Seizure Recognition Using EEG Signals

A Fusion of CNN and Grey Wolf Optimization-Enhanced BiGRU fo...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Sagnik De Prithwijit Mukherjee Debstuti Biswas Anisha Halder Roy Institute of Radio Physics and Electronics University of Calcutta Kolkata India

Epilepsy, a profoundly impacting neurological disorder, afflicts an estimated 65 million individuals across the globe. Early detection of seizures, a critical aspect of epilepsy management, poses an ongoing challenge. This study introduces an innovative hybrid deep learning approach that integrates Convolutional Neural Networks (CNN) for automated feature extraction with Grey Wolf optimization-based Bidirectional Gated Recurrent Unit (BiGRU) networks to differentiate between three fundamental stages of epileptic seizures: preictal, interictal, and ictal, utilizing Electroencephalogram (EEG) signals. The Grey Wolf optimization algorithm is employed to determine the most suitable hyperparameters for configuring the BiGRU network. The research seeks to address this pressing need, ultimately promising faster and more effective treatment, which is essential for improving the lives of those with epilepsy. Importantly, the proposed method significantly advances the field of epileptic seizure detection by achieving an accuracy of 98.67%. The obtained Precision, F1-score, and Recall values are 98%, 97%, and 98% respectively.

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Automatic intelligent Model for Lung Tumor Detection using Medical Images 3

Automatic Intelligent Model for Lung Tumor Detection using M...

引用

3rd International conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Devraj, P V Ashwathy Kumar, S Manoj Sriram, S. Venkatesan, G. Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India PSNA College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Dindigul India Sri Sai Ram Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462754

The rising mortality toll from tumor is indicative of a growing global health crisis. To put it simply, a tumor is an unnatural proliferation of abnormal cells that invades healthy cells. Lung tumor (LT) cause cellular imbalances that negatively impact lung function. In this era, LT has been and will continue to be the leading cause of mortality owing to tumor. Early LT prediction is crucial, especially in densely populated and low-income nations. Standard clinical techniques, such as blood tests and other medicines, are used by specialists. Using recent advances in Deep learning (DL), it is possible to build an environmentally friendly prototype system for the prediction of LT. The goal of this study is to use DL to automate the process of detecting LT. Starting with data collecting and ending with model validation, this study's pipeline depicts the steps taken in the research process. Pre-processing steps like reshaping and resizing the data, building a DL model like a Convolutional Neural Network (CNN) and a Combined Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM), training and validating the model comes in between the data collection and model validation stages. The experimental findings indicates that the CNN-LSTM is effective for LT prediction, with accuracy and loss of 98.12% and 1.87%, respectively. © 2022 IEEE.

关键词： Convolution

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Using AI to Detect and Classify Suspicious Mobile Messages in Real Time

Using AI to Detect and Classify Suspicious Mobile Messages i...

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Manjunath Reddy Rakesh Pallerla Biometric Expert Principal Engineer Qualcomm Inc. San Diego USA Biometric Expert Qualcomm Inc. Telangana India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The exponential growth of mobile messaging platforms has led to a volume of suspicious and malicious messages that pose significant risks to user privacy, financial security, and information integrity. This paper presents a novel artificial intelligence (AI)-based method for the real-time detection and classification of suspicious mobile messages. Unlike the traditional techniques, the proposed system integrates a lightweight natural language processing model with advanced machine learning algorithms to analyze content, metadata, and contextual patterns in messages. The proposed system uses a hybrid methodology integrating semantic analysis, anomaly detection, and behavioral profiling for the effective detection of such attacks. Extensive experimentation on diverse datasets highlights the robustness and scalability of the model, which achieves high precision, recall, and real-time processing capabilities without significant computational overhead. This innovative solution is well-suited for deployment in mobile applications, offering a practical and scalable framework to safeguard users against evolving threats in digital communication ecosystems.

关键词： Privacy Accuracy Scalability Semantics Machine learning Real-time systems Natural language processing Robustness Safety Security

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Research on ventilation and noise reduction technology for substations based on deep learning

Research on ventilation and noise reduction technology for s...

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Energy and Power Engineering, Control Engineering (EPECE), International conference on

作者： Li Zhu Zixuan Zhu Wenxiu Jiang State Grid Shanghai Economic Research Institute Shanghai China Power China shanghai Electric Power Engineering Co. Ltd Shanghai China

ISBN: (数字)9798350366099

ISBN: (纸本)9798350366105

With the accelerated urbanization process and the continuous growth of power demand, substations are a critical component of the power system. However, the main noise sources in substations, such as main transformers, reactors, and cooling fans, have a significant impact on the surrounding environment. Therefore, this paper proposes an optimization design method for ventilation and noise reduction in substations based on deep learning. Firstly, the finite element method is used to simulate the ventilation and noise data of substations under multiple operating conditions to obtain sufficient samples. Secondly, the construction of the Convolutional Neural Network (CNN) model is completed, and the obtained data is used for model training. Finally, the optimization solution of parameter design is achieved using the SAC algorithm. The results show that the designed parameters for substations using this method can effectively reduce noise and comply with national standards.

关键词： Deep learning Substations Noise reduction Noise Urban areas Transformers Data models

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