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Cardio Vascular Disease Prediction and Classification Report Generation using data mining Technique 3

Cardio Vascular Disease Prediction and Classification Report...

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3rd international conference on Smart data Intelligence, ICSMDI 2023

作者： Jebaseeli, T Jemima Navin Kumar, M. Subagar, Angeleen Santhosh, A. Karunya Institute of Technology and Sciences Department of Cse Coimbatore641114 India

ISBN: (纸本)9781665464871

Cardiovascular disease is one of the primary reasons for death in the world today. It has evolved into one of the most challenging illnesses to identify. By a recent WHO research, heart disorders are on the rise. As a result, 17.9 million people die each year. As the population increased, this became increasingly difficult to diagnose and initiate treatment during the initial stages. When it comes to forecasting coronary heart disease, medical analysis of data encounters a huge challenge. Electronic health record systems are currently used to handle the data of patients in hospitals. The huge amount of information created by the medical industry is being misused. A new approach is required to reduce costs and accurately predict heart disease. Hospitals can use appropriate decision support systems to reduce the cost of clinical tests. Several types of research offer barely a glimpse of optimism for employing machine learning approaches for predicting cardiac disease. The proposed study suggests a unique strategy for finding key characteristics via a machine learning approach throughout this work, which would also improve the precision of cardiovascular risk diagnosis. Diverse characteristic correlations and classification algorithms are used to establish the statistical model. Using the Improved random forest with Hyper - parameters tweaking in the classification algorithm for cardiovascular disease, a better reliability with an acceptable accuracy of 94.5% has been obtained. This approach may be valuable to healthcare professionals in their treatment as a decision assistance system. © 2023 IEEE.

关键词： Classification (of information)

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Development of machine learning Model for Detection and Diagnosis of Alzheimer's disease. A Comprehensive Review 3

Development of Machine Learning Model for Detection and Diag...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Khan, Safdar Sardar Patil, Sunil Rkdf University Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350343274

New techniques of detection and diagnosis are needed for Alzheimer's disease (AD) is a major concern for society's health. machine learning (ML) has been identified as a viable approach in this context due to its capacity to allow the development of predictive models that can help in the identification of AD across its various phases. This paper provides an overview of current developments in developing ML models for application in Alzheimer's disease screening and detection. The paper opens with a discussion of the importance of early identification and recognition of Alzheimer's disease by detailing the disease's pathophysiology and describing its clinical signs. data from neuroimaging (e.g., MRI, PET scans), clinical data (e.g., cognitive evaluations, genetic information), and new biomarkers (e.g., markers in cerebrospinal fluid) are all discussed next. The report then delves into the development of ML methods in AD studies, showing the shift from classical statistical approaches to cutting-edge algorithms like deep learning and ensemble approaches. This article delves into the significance of feature selection and extraction in improving model performance and readability. Moreover, the paper discusses difficulties connected to the accessibility of data, the presence of bias, and the requirement of standardized datasets and evaluation criteria. The review's meat and potatoes are an in-depth look at the latest research and top-tier ML models for AD diagnosis and detection. There is a large variety of techniques that may be used to these models, including as Transfer learning from different domains, semi-supervised learning, supervised learning, and unsupervised learning. The article also discusses how multimodal data fusion techniques could help improve diagnostic precision. The evaluation places equal emphasis on the interpretability and therapeutic usefulness of models as they do on their development. The article talks about how to incorporate model predictions

关键词： Genes

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Empirical Research on Multifactor Quantitative Stock Selection Strategy Based on machine learning 3

Empirical Research on Multifactor Quantitative Stock Selecti...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Zhang, Chengzhao Tang, Huiyue Chengdu Polytechnic Chengdu China Southwestern University of Finance and Economics Chengdu China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In this paper, stock selection strategy design based on machine learning and multi-factor analysis is a research hotspot in quantitative investment field. Four machine learning algorithms including support vector machine, gradient lifting regression, random forest and linear regression are used to predict the rise and fall of stocks by taking stock fundamentals as input variables. The portfolio strategy is constructed on this basis. Finally, the stock selection strategy is further optimized. The empirical results show that the multifactor quantitative stock selection strategy has a good stock selection effect, and yield performance under the support vector machine algorithm is the best. With the increase of the number of factors, there is an inverse relationship between the fitting degree and the yield under various algorithms. © 2022 IEEE.

关键词： Decision trees

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machine learning in Wireless Communication 3

Machine Learning in Wireless Communication

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Chen, Dingxin Wei, Xuancheng Ni, Chenglan North China University of Technology Brunel London School Beijing China Nanjing University of Posts and Telecommunications School of Electronic and Optical Engineering Zunyi China Wuhan Haidian Foreign Language Experimental School Wuhan China

ISBN: (纸本)9798350316674

In recent years, the technology of wireless communication has developed rapidly. For instance, 5G and 6G are a next-generation communication technology. Because of the potential of 5G and 6 G, large quantities of data can be generated, which can be utilized to enhance the performance of neural networks. Recently, convolutional neural networks have achieved remarkable performance in various applications, including voice recognition, channel prediction, and so on. Recently, there have been a lot of studies in this field, including physical, radio, and wireless. Recently, great progress has been made in the area of deep learning in the physical layer. This research covers a broad spectrum of topics. A novel method for automatic modulation recognition is proposed in this paper. This paper gives a comparative analysis of traditional AMR and DL-based AMR, and also describes CNN arithmetic. In addition to CNN-based 2D AMRs, they are also included in Star Map, Time Rate Entry, Eye Graph, etc. In the following section, we will show you an AMR algorithm for RNNs. Finally, this paper sums up and forecasts its future. © 2023 IEEE.

关键词： Automatic Modulation recognition Recurrent Neural Network Convolutional Neural Network

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Analysis of the Mechanism of Generative Adversarial Networks learning Based on Gray Image 3

Analysis of the Mechanism of Generative Adversarial Networks...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Geng, Chaozhi 21st Century International School Beijing100080 China

ISBN: (纸本)9781510661301

machine learning has a crucial role in people's lives. machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results. © 2023 SPIE.

关键词： Generative adversarial networks

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Predicting Laptop Prices in the Tunisian Market Using data mining and machine learning Methods 3

Predicting Laptop Prices in the Tunisian Market Using Data M...

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3rd IEEE international conference on Signal, Control and Communication, SCC 2023

作者： Balti, Ala Yassin, Mannai Lakhoua, Mohamed Najeh Sayadi, Mounir University of Tunis National High School of Engineering of Tunis Ensit Research Laboratory LR13ES03 Sime Tunisia University of Carthage National Engineering School of Carthage ENICarthage Tunisia University of Carthage National Engineering School of Carthage ENICarthage Research Laboratory Smart Electricity & Ict SEICT Tunisia

ISBN: (纸本)9798350326390

The laptop industry has undergone swift expansion and technological enhancements, resulting in a diverse array of laptop models and capabilities to cater to a broad spectrum of consumer demands. However, for businesses, the task of identifying the most suitable laptop that aligns with specific requirements can be quite formidable. To tackle this challenge, this study introduces a solution centered around predicting laptop prices through the application of supervised machine learning, with a particular focus on utilizing the Gradient Boost method. The system attains an impressive predictive accuracy of 94% when compared against actual market prices. Moreover, this investigation delves into the connections between laptop prices and various attributes including model, RAM, storage types (HDD/SSD), GPU, and CPU. By making use of this system and comprehending these correlations, enterprises can streamline the laptop selection process. This research significantly contributes to the domain of laptop price prognostication, providing enterprises with the tools to make well-informed decisions and optimize their laptop acquisition strategies. © 2023 IEEE.

关键词： Forecasting

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Evaluation of the Performance of Methods for Classifying EEG Signal Processing 63

Evaluation of the Performance of Methods for Classifying EEG...

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63rd international Scientific conference on Information-Technology-and-Management-Science of Riga-Technical-University (ITMS)

作者： Litvinenko, Anna Simanovics, Janis Riga Tech Univ Inst Radio Elect Riga Latvia

ISBN: (数字)9798350399851

ISBN: (纸本)9798350399851

Over the past few decades the popularity of artificial neural networks has been continuously increasing in the fields of machine learning, data mining and pattern recognition. However, it has been suggested that in many scenarios artificial neural networks do not always deliver the best performance when compared to other data classification methods, like tree-based models. This article evaluates and compares the performance of different classification algorithms and methods employing electroencephalography data. Both artificial neural networks and classic methods are benchmarked and compared.

关键词： artificial neural networks electroencephalography machine learning random decision forests support-vector machines tree-based models

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data mining for news content: a case of Cantonese opera news topic modelling analysis 3

Data mining for news content: a case of Cantonese opera news...

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3rd international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Wang, Bifeng Xu, Xiaotong Chen, Haocih Xie, Xinyi Chen, Jiawei Liu, Qian Fu, Yong School of Education GuangZhou Academy of Fine Arts Guangdong Guangzhou510006 China School of Journalism and Communication Jinan University Guangdong Guangzhou510632 China School of Literature Zhejiang University Zhejiang Hangzhou310058 China College of Liberal Arts Jinan University Guangdong Guangzhou510632 China

ISBN: (纸本)9781510661301

Topic modelling approach is widely used for text data mining in NLP(Natural Language Processing). Text mining has been used for analysis of ICH (intangible cultural heritage), where Cantonese opera is a representative ICH of Lingnan culture. This study retrieved news content on Cantonese Opera and used machine learning analysis (LDA topic modelling) method to find out the distribution of the topics. Four main themes are concluded: the development, cooperation, and inheritance of Cantonese opera(taken up to 45.1% in all data);The traditional form(23.5%);Innovative forms(18.3%);Education and cultural inheritance of Cantonese opera(13.2%). This research further explored how to better promote Cantonese opera by analysing the topics as well as the data, and suggested that emphasis should be placed on the innovation of traditional elements in Cantonese opera, keeping them close to life, and education. © 2023 SPIE.

关键词： machine learning

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A study on license plate recognition based on convolutional neural network 3

A study on license plate recognition based on convolutional ...

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3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Shi, Xiangui Wang, Xiaole Chen, Tao Qian, Cong Liu, Yiming School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781510661363

With the rapid development of advanced technologies such as artificial intelligence and Internet of Things, intelligent traffic management has been widely used. License plate recognition technology is particularly important in intelligent traffic management as the unique identification of vehicles, but due to the influence of natural environment, recognition angle, image clarity, size of license plate and other factors, the traditional license plate recognition technology is less robust and difficult to accurately identify the number plate number. In this paper, we describe a license plate recognition technique implemented by constructing Convolutional Neural Networks (CNN), which is able to recognize license plate numbers more accurately and has certain significance for further research on deep learning in the field of intelligent traffic management. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image processing

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

3rd International Conference on Artificial Intelligence, Rob...

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3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

ISBN: (纸本)9789819721993

The proceedings contain 33 papers. The special focus in this conference is on Artificial Intelligence, Robotics, and Communication. The topics include: Design and Research of UAV Fire Emergency and Intelligent Inspection System in the Valve Hall of Converter Station;Development Overview and Application Prospects of Advanced UUVs;bisporus Mushroom 3D Reconstruction for Digital Twin of Smart Factory;Research on Automatic Music Generation Based on LSTM;specific Emitter Identification Method Based on Aggregation Model with Metric learning in Complex Field;fake Comment Detection Based on Generative Adversarial Networks;Shipping Risk Assessment Ship Inspection Method Based on Improved YOLOv5;spatiotemporal Prediction of Harmful Algal Blooms Based on Spatiotemporal Attention and Transformer;large-Scale Information Dissemination Algorithm Based on Random Mapping;Smoke Detection in Complex Scenery Using Improved Faster-RCNN;CoT-Enhanced CNN-ViT Fusion with Adaptive Token learning for Precise Facial Expression recognition;evaluation Method of Cognitive Level Based on machine learning and Wearable Device;ship Image Segmentation Based on Multi-dimensional Information and Feature Fusion;real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence;traffic Light Detection Based on Deep learning;robot Navigation Based on Reinforcement learning: An Overview;active Control Technology of Supply Chain Intelligent Work Robot Based on Speech Enhancement Technology;research on Security Technology of Power Dispatching data Network Based on Deep learning;An Improved RRT Path Planning Method Incorporating Deep Reinforcement learning for Space Multi-arm Robot;a Classification Denoising Algorithm Based on 3D Point Cloud Curvature;Comparative Analysis of the Effect of KPCA in PSO-GRU Combination Model.

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