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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是111-120 订阅

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Review Paper on Glaucoma Detection Using machine learning 5th

Review Paper on Glaucoma Detection Using Machine Learning

引用

5th international conference on Energy Systems, Drives and Automations, ESDA 2022

作者： Jha, Kushal Gokharu, Naman Mathur, Rohan Bhandari, Sachin Department of Computer Engineering Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS Shirpur Campus India

ISBN: (纸本)9789819701537

Glaucoma is one of the leading causes of vision loss worldwide. Glaucoma cannot be cured in its advance stages. So, early detection of disease has become an important factor in the medical field. Numerous studies quickly became clear that using different image processing methods, the retinal fundus picture could be uncovered. In this study, many automated glaucoma detection techniques were thoroughly reviewed various papers were compared on the basis of the methodologies they adopted for detecting glaucoma from 2D fundus images created using CDR. 85% of glaucoma cases can be accurately detected by the majority of machine learning algorithms. First, image segmentation techniques like Elliptical Hough transform and edge detection gave the region of interest, i.e. optic disc and cup. these extracted images were then given to the machine learning and deep learning models to detect presence of glaucoma in the fundus image of the eye. the most significant deep learning, machine learning, and transfer learning methods for analysing retinal images were reviewed, along with their benefits and drawbacks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Predicting Cardiovascular Disease in Patients with machine learning and Feature Engineering Techniques 5

Predicting Cardiovascular Disease in Patients with Machine L...

引用

5th international conference on signal processing and Information Security, ICSPIS 2022

作者： Tyagi, Sapna Sirohi, Preeti Maheshwari, Piyush IMT Ghaziabad Ghaziabad India IMT CDL Ghaziabad India Dubai Academic City The British University in Dubai Dubai United Arab Emirates

ISBN: (纸本)9781665492652

Cardiac disease prediction and detection are among the most difficult and important jobs encountered by medical practitioners. Heart disease can be caused by a range of factors, including a sedentary lifestyle, stress, alcohol, cigarette intake, and so on. the current prediction algorithms focus on forecasting the illness label though the likelihood of getting the condition is still unknown. this study is conducted to forecast the heart disease progression well in advance so that essential action can be taken before the condition becomes severe. As a result, the research proposes a model for predicting the likelihood of heart disease incidence using logistic regression capabilities. © 2022 IEEE.

关键词： Forecasting

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Development of an Independent Adversarial Sample Detection Model, Based on Image Features 5th

Development of an Independent Adversarial Sample Detection M...

引用

5th international conference on 3D Imaging Technologies—Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2023

作者： Xu, Long Beijing University of Aeronautics and Astronautics Beijing China

ISBN: (纸本)9789819751808

Independent adversarial sample detection is an important problem in the field of computer vision and machine learning, especially in the context of the widespread use of deep learning models. this can lead to misclassification and performance degradation of the model, so adversarial sample detection is crucial to ensure the reliability of the model. this research focuses on the development of an independent adversarial sample detection model based on image features. A new approach is proposed which does not rely on the original model but focuses on detecting adversarial features in the samples. the effectiveness and robustness of the proposed method is verified in extensive experiments. the model is able to detect independent adversarial samples with high accuracy, regardless of whether the adversarial samples are targeted at a specific deep learning model or not. In addition, the method demonstrates excellent performance on a variety of image datasets and applications in different domains. It is expected to enhance the robustness and reliability of deep learning models, and thus better cope with adversarial sample attacks in practical applications. this approach also has a wide range of applications for a variety of computer vision tasks and domains. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Restoration Analysis of Chinese Ancient Books Using machine learning: A Case Study of Southwest Minzu University Library 5th

Restoration Analysis of Chinese Ancient Books Using Machine ...

引用

5th international conference on 3D Imaging Technologies—Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2023

作者： Yihe, Aniu Xie, Quanying Yu, Zhen Shi, Jianlan Library of Southwest Minzu University Chengdu China Preparatory Education College of Southwest Minzu University Chengdu China School of Tourism History and Culture Southwest Minzu University Chengdu China Shaanxi Academy of Social Sciences Xi’an China

ISBN: (纸本)9789819751808

this study aims to explore the potential application of machine learning in restoration of ancient books, and how it compares with traditional methods. through the analysis of 2000 ancient book data, we found that the machine learning method has shown obvious advantages in terms of time efficiency, cost saving, reproducibility, and adaptability. However, this approach also faces limitations such as data dependence, complex damage challenges, and generalization issues. In the actual application case of the library of Southwest Minzu University, the machine learning method has shown that it can efficiently repair damage to ancient books such as water damage and sunburn, which greatly shortens the repair time and saves a lot of cost compared with traditional methods. the comprehensive comparative analysis results show that the machine learning method can achieve similar or better restoration results than traditional methods in most cases. For libraries and cultural heritage institutions, this paper recommends further investment in machine learning technology research and development, as well as related data collection and staff training. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Human-machine Co-Driving Mode Switching Method of Autonomous Vehicles based on Traffic Scenarios 5

Human-Machine Co-Driving Mode Switching Method of Autonomous...

引用

5th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Zhang, Rui Huang, Zichao Fan, Qinglan Dan, Zhiwan Research Institute of Highway Transportation Industry Key Laboratory of Intelligent Transportation Technology Ministry of Transport Beijing China School of Automotive and Transportation Tianjin University of Technology and Education Tianjin China Tianjin Dolphin Intelligent Driving Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798350336030

Human-machine co-driving is a commonly used vehicle control mode in automatic driving. the human-machine co-driving mode switching model of multi-objective autonomous vehicles is designed in traffic scenes. the ADHDP iterative interactive verification is carried out to realize the human-machine co-driving mode switch of autonomous vehicles. the final test results showed that test in different target driving distance, comparing with the traditional hybrid switching test groups, characteristics of target handoff method finally concluded relatively fast switching speed. It also shows that this design method has better stability, switch in the switching process, fast, can achieve smooth switching smoothly, has a certain practical significance. © 2023 IEEE.

关键词： Autonomous vehicles

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An Analysis on Characteristics of Genitive in Tibetan Function Words Based on Knowledge Base 5

An Analysis on Characteristics of Genitive in Tibetan Functi...

引用

5th international conference on machine learning and Natural Language processing (MLNLP)

作者： Cairang, Suonan Duojie, Gongque Qinghai Normal Univ Coll Comp Xining 810016 Qinghai Peoples R China Minist Educ Key Lab Tibetan Informat Proc Xining 810008 Qinghai Peoples R China Tibetan Intelligent Informat Proc & Machine Trans Xining 810008 Qinghai Peoples R China

ISBN: (纸本)9781450399067

Tibetan function words play an extremely important role in the syntax, with high frequency of use and complicated usage. At present, the existing research results of Tibetan function words are subjective and ambiguous in the description of vocabulary traits, which is difficult to apply directly to the study of natural language processing. From the perspective of natural language processing, this paper analyzes the characteristics of Tibetan function words genre according to classification system and formal description of free function words in Tibetan language, and puts forward the preliminary ideas and solutions to specification of Tibetan function words.

关键词： Knowledge base Tibetan function words genitive feature analysis

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Forecasting Stock Exchange Trends for Discrete and Non-discrete Inputs Using machine learning and Deep learning Techniques 5th

Forecasting Stock Exchange Trends for Discrete and Non-discr...

引用

5th international conference on Soft Computing and signal processing, ICSCSP 2022

作者： Dhondi, Teja Ravi, G. Vazralu, M. Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789811986680

the trend of stock exchange prices is always uncertain for the stockholders, and venture capitalists is there governed by multiple factors that can influence them. In this project, we aim to predict the stock market trends using deep learning and machine learning techniques. We had chosen four distinct stock market groups from current stock exchange to be our dataset for experimentation and evaluation. they are metals, petroleum, non-metallic mineral and financials. In this paper, we try to build machine learning models for stock market correction using 9 different machine learning algorithms like SVM, KNN, ANN, RNN, LSTM, logistic regression, XGBoost, AdaBoost, etc. Ten different technical indicators from the past 10 years are used to train our machine learning models to predict the stock exchange movement in two ways, i.e., using non-discrete and discrete data. Our experimentation results reveal that for non-discrete data, the models built using RNN and LSTM give better performance bi for discrete data deep learning techniques outperform other mechanisms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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Data Characterization for Reliable AI in Medicine 5th

Data Characterization for Reliable AI in Medicine

引用

5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Rajaraman, Sivaramakrishnan Zamzmi, Ghada Yang, Feng Xue, Zhiyun Antani, Sameer K. NIH Bethesda MD 20894 USA

ISBN: (纸本)9783031235986;9783031235993

Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.

关键词： Data characteristics Artificial intelligence machine learning Deep learning Medical imaging Data-driven design Reliability Generalizability Robustness

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Monitoring Water Quality Based on Potability Using machine learning and IoT 5th

Monitoring Water Quality Based on Potability Using Machine L...

引用

5th international conference on Energy Systems, Drives and Automations, ESDA 2022

作者： Naik, Aayush Agarwal, Raghav Jain, Utkarsh Bhandari, Sachin Computer Engineering Department Narsee Monjee Institute of Management Studies Shirpur India

ISBN: (纸本)9789819701537

Around the world, water contamination is a big problem. Water quality needs to be maintained. the proposed work explores the development of a real time, cost-effective, and sustainable system for water quality monitoring. the proposed system uses various sensors to calculate several physical, as well as chemical properties of water. After examining various water sample properties, the proposed system experimented used a variety of machine learning techniques, including Support Vector machine, Time Series, Naives Bayes, K-means, etc. In this proposed system the water sample is collected from a variety of lakes, rivers, purifiers, and rainfall systems from a particular region. Collected water samples are processed by the Arduino model then the values are measured using the sensors. After processing the water samples will be categorized into 4 properties, i.e., TDS, pH temperature, and turbidity. this sensor data is further given to machine learning models. For predictive analysis, accuracy is calculated using the sensor data that is experimented on various machine learning models based on the standard drinking water range. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Method for Wireless Communication Interference signal Identification Based on Extreme learning machine 5

A Method for Wireless Communication Interference Signal Iden...

引用

5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Liu, XiaoZheng Wang, Yue Wang, XiaoFei Geng, Jian Beijing Institute of Space Long March Vehicle China Academy of Launch Vehicle Technology Beijing100076 China

ISBN: (纸本)9781510662520

Intelligent anti-jam communication is a new generation of anti-interference technology combined with artificial intelligence, and the identification of interference signals is the basis of the technology. It is required to achieve better identification results with lower computational complexity in engineering applications. However, previous research has shown that they cannot balance these two sides. Here, we report an interference signal identification algorithm based on Extreme learning machine (ELM). Five typical oppressive interference signals were recognized based on ELM which is based on feature extraction. the overall correct identification rate is more than 96% under the condition of 40 neurons in a single hidden layer, and it has certain generalization ability. this study objectively promotes the engineering application of this technology. © 2023 SPIE.

关键词： signal interference

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