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Automation of Mechanical Ventilation for Optimal Pressure Predictions Using machine learning 4th

Automation of Mechanical Ventilation for Optimal Pressure Pr...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Ghorpade-Aher, Jayshree Ijeri, Srushti School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Maharashtra Pune India

ISBN: (纸本)9789819994410

the onset of Covid-19 has brought a lot of concerns in the new era. this has affected a majority of people across the globe and has resulted to a significant rise in the number of patients who are also suffering from acute respiratory distress syndrome (ARDS), severe acute respiratory syndrome (SARS), and other types of respiratory failures which calls for the need of mechanical ventilation. the accurate prediction of the settings for mechanical ventilation along with the response of the lungs of the patient toward the adjustments of pressure will be helpful in improving the efficiency of the medical care treatment. A particular kind of sequence prediction problem is the prediction of lung pressure. the most effective technique for solving the sequence prediction problem is called Long Short-Term Memory (LSTM). LSTM has an advantage over standard Recurrent Neural Network (RNN), since it can selectively recall patterns over time. thus, the proposed algorithmic approach addresses the challenges of pressure prediction in mechanical ventilation and depicts healthcare assistance with the optimal pressure predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Human Activity Recognition in Videos Using Deep learning 4th

Human Activity Recognition in Videos Using Deep Learning

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4th international conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Kumar, Mohit Rana, Adarsh Ankita Yadav, Arun Kumar Yadav, Divakar NIT Hamirpur Dept Comp Sci & Engn Hamirpur 177005 HP India

ISBN: (纸本)9783031276088;9783031276095

Human Activity Recognition (HAR) is a challenging classification task. In the past, it traditionally involved the identification of the movement and activities of a person based on sensor inputs, apply signal processing to receive features and fit the features into a machine learning model. In recent times, deep learning methods have shown good results in automatic Human Activity Recognition. In this paper, we propose a pre-trained CNN (Inception-v3) and LSTM based methodology for Human Activity Recognition. the proposed methodology is evaluated on the publicly available UCF-101 dataset. the results show that the proposed methodology outperforms recent state-of-art methods in terms of accuracy (79.21%) and top-5 accuracy (92.92%) on the HAR task.

关键词： Human Activity Recognition HAR LSTM CNN Inception V3 UCF-101

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Traffic Noise Modeling in Sambalpur City Using machine learning Technique 4th

Traffic Noise Modeling in Sambalpur City Using Machine Learn...

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4th international conference on Recent Advances in Mechanical Engineering Research and Development, ICRAMERD 2023

作者： Meher, K. Majhi, S. Khandualo, S.K. Pradhan, P.K. Deaprtment of Mechanical Engineering VSSUT Odisha Burla India Deaprtment of Computer Science and Engineering VSSUT Odisha Burla India Odisha Hydro Power Corporation Odisha Burla India

ISBN: (纸本)9789819710799

Noise is defined as sound that is considered unpleasant, loud, or disruptive to hearing. Similar to air pollution, noise pollution is a significant issue in urban areas that demands attention. Noise pollution occurs when the noise level surpasses a specific threshold and negatively impacts human health and well-being. In the field of urban planning, it becomes crucial to utilize methods and tools that can aid designers in developing, planning, and implementing appropriate measures to mitigate and control traffic noise. Consequently, it is essential to predict traffic noise levels in order to prevent excessive noise and explore potential alternative suggestions for existing road infrastructure. the objective of this study is to develop a predictive model for traffic noise level using linear regression analysis in the field of machine learning. A suitable model has been formulated using linear regression to accurately predict the traffic noise level. Subsequently, the model was tested and validated in the city of Sambalpur. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Noise pollution

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machine learning Assisted Software Transplantation: A Baseline Technique 1

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4th international conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Sodhi, Gurjot Singh Rattan, Dhavleesh Department of Computer Science and Engineering Punjabi University Punjab Patiala147002 India Iconic Trainer Koenig Solutions Pvt. Ltd. Gurugram India

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

Paradigm and research work culture have evolved with the ontogenesis of the software development field throughout the timeframe because of their heterogeneity. Genetic Improvement (GI), which views the code as manipulable "genetic material," is the foundation of the transplantation approach. We must capture the code in the DONOR that the selected functionality depends on in order to transplant it to an unrelated HOST. So, we espouse two eccentric frameworks in the realm of neural networks, each with its idiosyncrasy;PyTorch framework (which is notorious for deployment) and TensorFlow emphasis the domain of Artificial Intelligence. this transplantation is more than 3000 times faster than re-training. Our baseline technique took 20 times less time in contrast with ONNX Convertor. this work demonstrates that for any existing exemplar with acceptable outcomes, we don’t have to begin from scratch, nor do we need to inculcate any convertors. We also proposed the need for software transplantation and the circumstances under which this transplantation would be incredibly useful. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of machine learning Classifiers 4th

Resampling Strategies for Mitigating Class Imbalance of ASD ...

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4th international conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Gupta, Rahul Kumar Dutta, Kamlesh School Of Technology Woxsen University Hydrabad India Department of Computer Science & Engineeing National Institute of Technology Hamirpur HP Hamirpur India

ISBN: (纸本)9789819942831

In the supplied ASD dataset, it is typically seen that there is an extremely large imbalance in the number of samples for two classes, leading to an imbalance. Without addressing this issue, applying binary classification algorithms to such data would produce an extremely biased result. It affects the relationships between features as well. these misclassifications could affect the decision regarding medical treatment and result in a protracted delay for those who urgently require medical intervention. In the current study, we use a variety of resampling strategies to address the issue of class imbalance. Precision, Recall, and F1-score are used as evaluation measures for all models. We have also looked at AUC score, which demonstrates encouraging outcomes for the use of resampling methods for imbalanced dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： machine learning

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An Intrusion Detection Method Based on Transfer Component Analysis and an Extreme learning machine 10

An Intrusion Detection Method Based on Transfer Component An...

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10th international conference on computer and Communications, ICCC 2024

作者： Yang, Baoqiu Zhang, Guoyin Wang, Kunpeng Gong, Jianguang College of Computer Science and Technology Harbin Engineering University Harbin China Technology Innovation Center Harbin Construction Engineering Group Co. Ltd Harbin China

Intrusion detection technology is an active network defense technology that has quickly become an important means of ensuring network security since it was proposed. With the development of machine learning, its application in intrusion detection has effectively improved the detection effect. However, due to the increasing volume and dimension of the current network data, the model training speed of machine learning-based intrusion detection technology is slow, and the update cost is high. In addition, different distributions of intrusion detection data affect the detection effect. In this paper, the extreme learning machine (ELM) model is extended. An intrusion detection method, TCA-ELM, based on transfer component analysis (TCA) and an extreme learning machine is proposed. TCA-ELM utilizes TCA to extract data features and project data to a new space by greatly reducing the distance and dimension between the data distributions and then trains the ELM in the new feature space to improve the training efficiency and detection rate. Experimental tests are performed on the famous NSL-KDD dataset, and the experimental results demonstrate the effectiveness of the proposed TCA-ELM method. Compared with the other four benchmark algorithms, the training time is significantly improved, and the detection accuracy is also significantly improved. © 2024 IEEE.

关键词： data distribution data features extreme learning machine intrusion detection network security

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machine learning Approach to Crisis Management Exercise Analysis: A Case Study in SURE Project 3rd

Machine Learning Approach to Crisis Management Exercise Ana...

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3rd international conference on Intelligent Edge Processing in the IoT Era, Edge-IoT 2022, and the 4th international conference on Smart Governance for Sustainable Smart Cities, SmartGov 2022

作者： Joutsijoki, Henry Mäenpää, Sari Karppi, Ilari Sankala, Iina Insta Advance Oy Sarankulmankatu 20 Tampere33900 Finland Faculty of Management and Business Tampere University Kanslerinrinne 1 Tampere33014 Finland

ISBN: (纸本)9783031359811

Crisis management exercises play a key role in preparation for different scenarios such as natural disasters, or large crowd events. Moreover, they act as a platform for testing new technologies and finding common practices between different actors. In this preliminary paper, we apply a collection of machine learning methods to a questionnaire data to find factors, where authorities’ and non-authorities’ perspectives differ when a large number of questions are covered simultaneously. Such factors may be focal elements in hindering the co-operation between different actors in a real-world case. therefore factors are essential to be revealed. Feedback data was collected from a multi-organizational crisis management exercise that was organized as part of an EU funded Smart Urban Security and Event Resilience (SURE) project. Dataset was examined in four question set scenarios and in each case we obtained around 80% accuracy when classifying authorities and non-authorities. this indicates that parties’ perspectives differ about the crisis management exercise and it strengthens the idea of recognizing the possible obstacles of co-operation. Although crisis management exercises are relatively well researched area in the literature, the use of machine learning methods in this context gives a novel approach for analyzing the results. machine learning based analysis of results enables improving crisis management exercises in the future. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and DeBERTa Transformer Model with Contrastive learning for Causal Event Classification 6

CSECU-DSG @ Causal News Corpus 2023: Leveraging RoBERTa and ...

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6th Workshop on Challenges and applications of Automated Extraction of Socio-Political Events from Text, CASE 2023

作者： Hossain, Md. Akram Aziz, Abdul Chy, Abu Nowshed Department of Computer Science and Engineering University of Chittagong Chattogram4331 Bangladesh

ISBN: (纸本)9789544520892

Cause-effect relationships play a crucial role in human cognition, and distilling cause-effect relations from text helps in ameliorating causal networks for predictive tasks including natural language-based financial forecasting, text summarization, and question-answering. However, the lack of syntactic clues, the ambivalent semantic meaning of words, and complex sentence structures make it one of the challenging tasks in NLP. To address these challenges, CASE-2023 introduced a shared task 3 with two subtasks focusing on event causality identification with causal news corpus. In this paper, we demonstrate our participant systems for this task. We leverage two transformers models including DeBERTa and Twitter-RoBERTa along with the weighted average fusion technique to tackle the challenges of subtask 1 where we need to identify whether a text belongs to either causal or not. For subtask 2 where we need to identify the cause, effect, and signal tokens from the text, we proposed a unified neural network of DeBERTa and DistilRoBERTa transformer variants with contrastive learning techniques. the experimental results showed that our proposed method achieved competitive performance among the participants’ systems and achieved 4th and 3rd rank in subtasks 1 and 2 respectively. © CASE 2023 - proceedings of the 6th Workshop on Challenges and applications of Automated Extraction of Socio-Political Events from Text, associated with 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023.

关键词： Natural language processing systems

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Research on the application of computer Artificial Intelligence Evolutionary Game Technology in the Prevention of Big Data Killing System 4

Research on the Application of Computer Artificial Intellige...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Liang, Tian Shanghai University School of Management Shanghai200444 China

ISBN: (纸本)9798350342710

In this paper, the government regulation as the lead to establish the e-commerce platform "big data killing"pricing behavior and user consumption channel selection evolutionary game model. then this paper based on the "java"backend design language and front-end design language "WXML"design and development of a set of price comparison small program. the system can display and compare the prices of the same goods or services on different e-commerce platforms in real time. this makes it easy for consumers to buy at the most favorable price. the evolutionary stability strategies of consumers and merchants are analyzed systematically, and the results are simulated by MATLAB software. the simulation results show that the ratio of price-sensitive old users and sticky new users as well as the fine of regulatory authorities are the key factors affecting the stable state of the evolutionary game system. Transfer costs and psychological losses can influence users' willingness to choose a "retention"strategy. © 2023 IEEE.

关键词： Big data

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AFL-CS: Asynchronous Federated learning with Cosine Similarity-based Penalty Term and Aggregation 29

AFL-CS: Asynchronous Federated Learning with Cosine Similari...

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29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yan, Bingjie Jiang, Xinlong Chen, Yiqiang Gao, Chenlong Liu, Xuequn Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China University of Chinese Academy of Sciences Beijing China Nanchang Aier Eye Hospital Nanchang China

ISBN: (纸本)9798350330717

Horizontal Federated learning offers a means to develop machine learning models in the realm of medical application while preserving the confidentiality and security of patient data. However, due to the substantial heterogeneity of the devices in medical institution, traditional synchronous federated aggregation methods result in a noticeable decrease in training efficiency, thereby impacting the application and deployment of federated learning. Asynchronous Federated learning (AFL) model aggregation methods can mitigate this problem but present new challenges in terms of convergence stability and speed. In this paper, we propose a cosine similarity-based layer-wise penalty term and asynchronous model aggregation method AFL-CS, which considers the global model convergence direction during local training. Compared with existing AFL aggregation methods, AFL-CS can achieve faster and more consistent convergence direction to superior performance especially in non-iid settings with high statistical heterogeneity, even reaching and exceeding synchronous FL. © 2023 IEEE.

关键词： asynchronous federated learning medical application model aggregation privacy protection

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