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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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application of machine learning Algorithm in Art Field – Taking Oil Painting as an Example 1

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3rd international conference on Big Data and Security, ICBDS 2021

作者： Huang, Kun Jiang, Jianlong Anhui Xinhua University Anhui230088 China

ISBN: (数字)9789811908521

ISBN: (纸本)9789811908514

Oil painting production is a very time-consuming task. This article uses the current generation confrontation network popular in machine learning to transfer the style of images, and directly convert real-world images into high-quality oil paintings. In view of the current popular AnimeGAN and CartoonGAN generative confrontation networks, there are problems such as serious loss of details and color distortion in image migration. In this paper, by introducing SE-Residual Block (squeeze excitation residual block), comic face detection mechanism and optimizing the loss function, a new BicycleGAN is proposed to solve the problem of serious loss of details in the AnimeGAN migration image. By adding DSConv (distributed offset convolution), SceneryGAN is proposed to speed up the training speed and eliminate the ambiguous pixel blocks in the CartoonGAN migration image. The experimental results show that compared with AnimeGAN and CartoonGAN, the method in this paper has a significant improvement in training speed, comic image generation quality, and image local realism. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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machine learning Approaches for Lung Cancer Prediction 12

Machine Learning Approaches for Lung Cancer Prediction

引用

12th international conference on Advanced computer Information Technologies, ACIT 2022

作者： Celik, Alpre Emre Rasheed, Jawad Yahyaoui, Amani Istanbul Aydin University Department of Computer Engineering Istanbul Turkey Istanbul Sabahattin Zaim University Department of Software Engineering Istanbul Turkey

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

Cancer is a long-term, exhausting disease that requires changes in all living conditions of the patient and his/her environment. Although there are regional variations in deaths from all causes in the world, it is in the 3rd rank. Lung cancer is among the most frequent cancer kinds worldwide, regardless of male or female. Cancer is a preventable disease. To prevent a disease, it is necessary to know its causes and avoid them. The use of tobacco and tobacco products is the main risk factor for all cancers, especially lung cancer. Early diagnosis of cancer is lifesaving. According to the Turkish Respiratory Research Association, 200,000 people are diagnosed with cancer every year in our country. With the accelerated developments in technologies and the digitalization of health services, a large amount of cancer data has been collected and this data has been used by many researchers, especially in low and middle-income countries, to reduce the cost of tests used to predict different cancer types and to predict different cancer types. This article is exploited various machine learning algorithms for predicting lung cancer. Experimental results show that random forest performed better by attaining 96.08% accuracy. © 2022 IEEE.

关键词： Tobacco

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machine learning Models for Automated Heart Disease Prognosis 3

Machine Learning Models for Automated Heart Disease Prognosi...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Ghoualmi, Lamis Benkechkache, Mohamed El Amine Ghoualmi, Abderraouf Djalil University of Constantine 2 Department of Computer Science Constantine Algeria University of Tennessee Department of Nuclear Engineering TN United States University of Algiers 1 Dapartement of Medicine Algiers Algeria

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

As reported by the Centers for Disease Control and Prevention (CDC), heart disease is the major cause of death for both sexes and members of most racial and ethnic groups. Cardiovascular disease claims one life in the United States every 34 seconds. The expense of medical services, medications, and lost productivity because of deaths from heart disease causes the United States to lose a significant amount of money each year. At least half of all Americans have one or more of the following three major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other significant indicators are the presence of diabetes, obesity (high body mass index), a lack of physical activity, and excessive alcohol consumption. In recent times, the healthcare sector has discovered that machine learning is a practical and reliable decision-making tool. The latter tends to assist doctors in making informed decisions about the heart condition of a patient. It has become increasingly important to develop an effective early-stage cardiac disease prognosis using digital patient information to enhance clinical decision-making. This study intends to find the most accurate machine learning classifiers for these prognostic uses. To predict cardiac disease, several supervised machine-learning algorithms were used and compared in terms of accuracy and performance. The following methods were used in our research: Logistic Regression, Random Forest, Decision Tree, and K Nearest Neighbor. The heart disease dataset from Kaggle has been used to validate this research. The latter was created using data from the 2020 United States national health status survey of more than 410,000 people, performed yearly by the centers for disease control and prevention. © 2022 IEEE.

关键词： Decision trees

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Deep learning algorithm vs XGBoost using Wisconsin breast cancer diagnosis 3

Deep learning algorithm vs XGBoost using Wisconsin breast ca...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Bakar, Wan Aezwani Wan Abu Zuhairi, Muhamad Amierusyahmi Man, Mustafa Jusoh, Julaily Aida Josdi, Nur Laila Najwa Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut22200 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia

ISBN: (纸本)9781510661240

Breast cancer is causing a significant increase in the number of deaths every year. It is the most common kind of cancer and the leading cause of death in women all over the world. Any improvement in cancer illness prediction and detection is critical for a healthy life. As a result, high accuracy in cancer prediction is critical for keeping patients' treatment and survival standards up to date. machine learning and deep learning approaches, which have been shown to have a significant impact on the process of breast cancer prediction and early diagnosis, have become a research hotspot and have been proven to be a powerful technique. We went through one machine learning method, XGBoost and deep neural network in this research on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. After getting the outcomes, these distinct classifiers' performance is evaluated and compared. The major goal of this research is to use machine-learning and deep learning algorithms to predict and diagnose breast cancer, and to determine which algorithms are the most efficient in terms of confusion matrix, precision, and accuracy. © 2022 SPIE.

关键词： Forecasting

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3rd international conference on Industry 4.0 and Smart Manufacturing, ISM 2021

3rd International Conference on Industry 4.0 and Smart Manuf...

引用

3rd international conference on Industry 4.0 and Smart Manufacturing, ISM 2021

The proceedings contain 195 papers. The topics discussed include: simulation of temperature control and irrigation time in the production of tulips using fuzzy logic;machine learning-based digital twin for monitoring fruit quality evolution;data science - time series analysis of oil & gas production in Mexican fields;characterizing lifetime estimation of fusing lamps in multi-jet fusion 3D printers;interactive sensor dashboard for smart manufacturing;customer-induced planning deviations within order management;mathematical modelling of waveguide paths by electron-beam welding;and TRAINMAN-MAGOS: capture of dexterous assembly manufacturing know-how as a new efficient approach to support robotic automation.

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Third international conference on computer Communication and Network Security, CCNS 2022

Third International Conference on Computer Communication and...

引用

3rd international conference on computer Communication and Network Security, CCNS 2022

ISBN: (纸本)9781510660113

The proceedings contain 51 papers. The topics discussed include: intrusion detection based on deep belief network and stacking method;the acquisition system incorporating inaccurate replacement under the information technology;an intelligent assessment method of power information intrusion tolerance based on machine learning;research on the fusion scheduling of functional safety and information security of relay protection device based on priority;the construction of legal education network database;anti leakage method of power information communication data based on chaotic mapping;research on security protection path of information infrastructure: how to identify the critical information infrastructure of the communication industry;edge network intrusion detection based on multi-center incremental clustering algorithm;cloud computing security and its application in power grid;and optimization of intelligent supply chain of metering materials based on data mining technology.

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Automated Medical Recommendation System using machine learning Techniques and Natural Language Processing 3

Automated Medical Recommendation System using Machine Learni...

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3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Likith, V.P. Begam, M. Farida Shashikant, Magadum Neeraj CMR Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9781665474979

The suggested work in this paper relates to the automation in the medical field in terms of how the diagnosis of irregularities has been going on in the industry and what the future in the said field looks like with the advancing technology. With the advent of application of machine learning approaches, we have observed a significant rise in the performance of detection and prediction with each passing year. With the current norms being that a patient is to perform the necessary tests and wait for a significant period of time to gain a consultation with an esteemed doctor. The methodologies used by other authors from the research papers that we gathered were either using image corpus or using machine learning techniques to detect and identify cancer, while others have used Natural Language Processing techniques to extract information from handwritten or printed texts and have developed models based on it. We have addressed the problem with a unified approach that combines both machine learning and Natural Language Processing techniques in tandem to extract data and correlations which may have been ignored by the other models. The image corpus and handwritten notes would together give better insights and help improve the accuracy of the model. Bearing in mind the objective of relieving novice doctors from feeling perplexed, in addition to answering questions that patients might have, our model aims to allow such medical professionals make better informed decisions in their diagnosis and prognosis procedures. The chatbot, to serve this purpose, will be based on Bidirectional Encoder Representations from Transformers (BERT), which has demonstrated ground-breaking performance in tasks involving natural language understanding, including sentiment analysis, semantic role labelling, sentence classification, and the disambiguation of polysemous words. We aim to not only successfully diagnose and provide prognosis for cancer, but also to detect the chances of remission. Our

关键词： BCRSVM BERT Decision Tree Graphs DenseNet121 Imputations Random Forest Regressor Semantic role labelling SHAP

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Social Media Personal Event Notifier Using NLP and Deep learning 3

Social Media Personal Event Notifier Using NLP and Deep Lear...

引用

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

作者： Ganeshkumar, Pavithiran Ashwin Kumar, B.R. Padmanabhan, Sharan Vetriselvi, A. Panimalar Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Institute of Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665462754

Social media apps have become very promising and omnipresent in daily life. Most social media apps are used to deliver vital information to those nearby and far away. As our lives become more hectic, many of us strive to limit our usage of social media apps because they are too addictive, and the majority of us have gotten preoccupied with our daily lives. Because of this, we frequently overlook crucial information, such as invitations to weddings, interviews, birthday parties, etc, or find ourselves unable to attend the event. In most cases, this happens because users are more likely to discover the invitation or information only before the event, giving them little time to prepare. To solve this issue, in this study, we created a system that will collect Social media chat and filter it using Natural Language Processing (NLP) methods like Tokenization, Stop Words Removal, Lemmatization, Segmentation, and Named Entity Recognition (NER). Also, machine learning Algorithms such as K-Nearest Neighbor (KNN) Algorithm are implemented to prioritize the received invitation and to sort the level of priority. Finally, a customized notification will be delivered to the users where they acknowledge the upcoming event. So the chances of missing the event are less or can be planned. © 2022 IEEE.

关键词： learning algorithms

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ML based Prototype for Skin Cancer Detection 3

ML based Prototype for Skin Cancer Detection

引用

3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Saju, Binju Asha, V. Murali, Sarath C Vinayaka, D. Kumar, Vikash Nithya, B. Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665474979

Skin malignant growth is one of the most lethal types of disease, and the demise rate has essentially become because of an absence of consciousness of the markers and protection measures. Therefore, in order to stop the spread of cancer, early identification at an early stage is essential. Skin cancer is further classified into several forms, with melanoma, nevus and seborrheickeratosis. This study utilizes Artificial Intelligence (AI) and picture-handling techniques to recognize and classify various types of skin cancer. Dermoscopic pictures are thought about as contributions during the pre-handling steps. The production of a Convolutional Neural Network based melanoma detection model. If melanoma is not found in its early stages, it can be fatal. It is responsible for 75% of skin cancer fatalities. A system that can analyse photos and notify dermatologists of the existence of melanoma might potentially eliminate the need for a lot of manual diagnosis work. Similar findings to those of the pertained model were produced by our created model. In simulations using the 2017 international Skin Imaging Collaboration skin cancer dataset, the suggested method performed admirably. The Convolutional neural network was able to achieve a 92 % accuracy rate. © 2022 IEEE.

关键词： Convolutional neural network deep learning ISIC skin lesion/cancer machine learning

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Image Recognition Methods Based on Deep learning 1

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3rd international conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep learning (3DIT-MSP and DL)

作者： Zhang, Zehua Univ Calif San Diego 9500 Gilman Dr La Jolla CA 92093 USA

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

With the development of science and technology worldwide, computer technology has gradually been widely used in various fields like the military and biomedical domains. In this situation, people's requirements for image recognition are increasing. However, the efficiency of manual image recognition is very low, and image recognition has always been a weak field in computer technology. This paper will introduce various deep learning models such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will talk about how these models work and then compare and analyze these different networks. In this paper, we will try to train a convolutional neural network from a data set. The "dogs-vs-cats" data set from Kaggle will be used. In the "dogs-vs-cats" experiment part, some basic tasks in image processing will be discussed, such as feature extraction method, which is one of the most important steps in each link of image processing. Finally, the machine should be able to distinguish between pictures of dogs and cats. Moreover, we will also discuss some preliminary applications of deep learning in image recognition.

关键词： Deep learning Image recognition machine learning

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