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

共 1857 条记录，以下是141-150 订阅

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Enhancing Medical Image-Based Diagnostics Through the application of Convolutional Neural Networks Techniques 3

Enhancing Medical Image-Based Diagnostics Through the Applic...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Byeon, Haewon Nair, Rajit Mahalakshmi, V. Khalaf, Mohammed I. Kaushik, Bharat Shabaz, Mohammad Inje University Department Of Ai And Software Gimhae50834 Korea Republic of School Of Computing Science & Engineering Vit Bhopal University Bhopal India College Of Engineering And Computer Science Jazan University Department Of Computer Science Jazan45142 Saudi Arabia Al Maarif University College Department Of Computer Science Al Anbar 31001 Iraq Masters University Of Northern Iowa IA United States Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques. © 2024 IEEE.

关键词： Diagnosis

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An improved Harris Hawk optimization algorithm and its application to Extreme learning machine 3

An improved Harris Hawk optimization algorithm and its appli...

引用

3rd international conference on Consumer Electronics and computer Engineering, ICCECE 2023

作者： Liu, Ziliang Chen, Hongwei School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9798350331578

The Harris Hawk optimization (HHO) algorithm is an excellent swarm intelligence optimization algorithm which has the advantages of high efficiency in finding the best, ease of implementation and wide application. It also has some disadvantages such as the possibility of convergence too fast and the tendency to fall into local optima. This paper combines an improved escape energy update approach and the leader update operator of the Salp Swarm Algorithm to improve the HHO, named IMHHO. The experiments show that the improvements have improved the algorithm's ability to find the best. IMHHO was also used in the parameter optimization of the Extreme learning machine, which also enables the ELM to find the right weights and bias values and to regress the data more accurately. © 2023 IEEE.

关键词： Optimization

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Incorporating text and emoticon features in sarcasm and irony detection: A deep learning-based approach 3

Incorporating text and emoticon features in sarcasm and iron...

引用

2023 IEEE 3rd international conference on Data Science and computer application, ICDSCA 2023

作者： Ma, Xinjun Li, Tianning Jin, Zhida Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China Shanghai Institute of Technology College of Computer Science and Information Engineering Shanghai China Northwest Normal University College of Computer Science and Engineering Lanzhou China

ISBN: (纸本)9798350341546

Emotions play a crucial role in social media interactions, and sarcasm and irony often convey complex emotions that can be challenging to detect accurately. This study focuses on automating the recognition of sarcasm and irony in Chinese through various computational methods using machine learning algorithms. This study utilizes both text and emoticon/emoji feature to improve detection accuracy. The study employs a combination of linguistic rules, word embeddings, and neural network models for detecting irony in Chinese microblog comments. The results highlight the advantages of deep learning techniques and pre-trained word embeddings in enhancing the accuracy of irony detection. Furthermore, the study demonstrates the viability of the proposed models by focusing on microblog posts related to social events. These contextualized experiments show promising results and pave the way for future studies to explore other domains and data sources. Overall, this study has made significant contributions to the field of irony recognition research and laid the groundwork for more complex models in the future, with practical implications for enhancing sentiment analysis in various fields. © 2023 IEEE.

关键词： deep learning irony detection machine learning sarcasm detection sentiment analysis

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applications of Data Science and machine learning for Combating COVID-19 3rd

Applications of Data Science and Machine Learning for Combat...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Tyagi, Shiva Sirohi, Shagun Singh, Yash Vishwakarma, Akash Siddhant Ajay Kumar Garg Engn Coll Dept Comp Sci & Engn Ghaziabad India

ISBN: (纸本)9783031640636;9783031640643

Global research efforts have increased dramatically as a result of the COVID-19 epidemic, with data science, machine learning, and deep learning techniques emerging as essential weapons in the fight against this crisis. In order to clarify the current research trends in the application of these cutting-edge technologies to battle COVID-19, we conduct an extensive scientometric analysis in this research paper that spans from January 2020 to April 2020. Our analysis not only identifies the main lines of inquiry, but also explores their broad ramifications and provides perceptive glimmers into the changing COVID-19 research scene. We identify the emerging topics, approaches, and applications that have proven essential in combating the pandemic by carefully examining a variety of scholarly articles. This report highlights the varied contributions of data science and machine learning to this global health problem, ranging from early detection models to vaccine development approaches. Additionally, our work goes beyond the current environment to set the way for additional research projects. We put up fresh ideas that make use of the strength of data-driven strategies to address the COVID-19's enduring problems. This research paper offers a roadmap for researchers, policymakers, and practitioners to use data science and machine learning in their ongoing efforts to combat COVID-19 and get ready for Upcoming pandemics by combining scientific rigor and computational innovation.

关键词： Data Science machine learning Deep learning COVID-19 Epidemic Scientometric Analysis Research Trends Pandemic Early Detection Models

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machine learning and Data Mining 1

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3rd international conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Kurasov, Dmitry A. Kutuzov, Anton S. Zvonarev, Dmitry S. Devyatkov, Anton P. University of Tyumen 6 Street Volodarskogo Tyumen625003 Russia Chelyabinsk State University 129 Street Kashirin Brothers Chelyabinsk454001 Russia

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

The article discusses the main tasks of machine learning. The functional structure of a computer algorithm for solving machine learning problems and a data mining model are considered. The solution of the simplest machine learning problem using the classification method of linear regression is proposed. The analysis of existing learning algorithms based on a decision tree is carried out. Based on the analysis performed, a decision tree was selected for implementation using the so-called C4.5 algorithm. The article builds a decision tree using specific training data. The application of a simple and understandable algorithm for building trees is to create all possible trees, calculate the number of erroneously classified data for each of them and select a tree with a minimum number of errors. As a result, an optimal learning algorithm is formed for the decision tree in terms of data errors in the learning process. The top-down algorithm implemented in the article for building a decision tree selects the attribute with the largest increase in information at each step. Entropy is used as a metric of the amount of information in the training data set D. In the process of implementation, this algorithm is analyzed to identify opportunities for its application to a more complex task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Entropy

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Forecasting Cardiovascular Disease Using Deep learning 3

Forecasting Cardiovascular Disease Using Deep Learning

引用

3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Singh, Dalwinder Singh, Arun Rakhra, Manik Singh, Janpreet Kumar, Rakesh Lovely Professional University Department of Computer Science & Engineering Jalandhar Phagwara India Lovely Professional University Department of Mathematics Punjab Phagwara144411 India

ISBN: (纸本)9798350303360

Across the world most of the deaths are caused due to heart disease. To lower death rates, early heart disease identification and prevention are essential. In recent years, early detection and cardiac disease prediction have both benefited from the machine learning techniques application. With the use of the *** dataset, this study compares how well Support Vector machine (SVM), Random Forest (RF) and Decision Tree (DT) do at forecasting heart disease. The findings show that Random Forest outperforms SVM and Decision Tree with 100% accuracy. © 2023 IEEE.

关键词： Support vector machines

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Disease Prediction Using Random Forest Classifier by machine learning application 3

Disease Prediction Using Random Forest Classifier by Machine...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Singh, Sumit Mohd, Noor Shukla, Tanmay Sharma, Jaya Chopra, Aarushi Graphic Era Deemed to Be University Computer Science & Engineering UK Dehradun India

ISBN: (纸本)9798350322842

Accurate disease prediction is a critical task in healthcare, yet it can be particularly challenging due to the complexity of the human body and the multitude of factors that contribute to the onset of various diseases. Fortunately, machine learning algorithms such as the random forest classifier have been shown to be valuable tools in predicting diseases. This research study aims to explore the application of random forest classifiers in disease prediction, specifically by analyzing their performance in predicting different types of diseases using various symptoms. Our study found that the random forest classifier is a powerful and reliable tool for disease prediction, producing precise and trustworthy results. With its proven efficacy and versatility, the random forest classifier is poised to become an essential part of the disease prediction toolkit for healthcare professionals. © 2023 IEEE.

关键词： Decision trees

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Artificial Intelligence Technique for Rice Seed Disease and Quality Assessment 3

Artificial Intelligence Technique for Rice Seed Disease and ...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hassan, B. Nazia Somashekara, M.T. Government First Grade College Vijayanagar Department of Computer Science Karnataka Vijayanagar India Bangalore University Department of Computer Science and Applications Karnataka Bangalore India

ISBN: (纸本)9798350319125

Ensuring high-quality rice seeds is vital for agricultural productivity. This research explores existing methods for assessing rice seed quality, land suitability, and seed disease in rice. A survey of the literature reveals a diverse range of techniques used for these purposes. In particular, this study delves into the application of machine learning (ML) classifiers and deep learning (DL) techniques for assessing the rice seed quality. This paper, exhaustively covers all major work based on ML and DL techniques. Through extensive analysis, the research assesses these methods in terms of accuracy, precision, recall, F1-score, error rates, and the kappa coefficient. The results demonstrate that both ML and DL exhibit superior performance, showcasing their potential for enhancing rice seed quality assessment and disease detection. This research contributes valuable insights into the application of advanced technologies in agriculture, with implications for improving crop yield and food security. This paper serves as aid for researchers working in this area. © 2023 IEEE.

关键词： computer Vision Deep learning machine learning Rice Seed Quality

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Flood Location, Management and Solution(FLMS): A Flood Prediction and Management System for Kurla 3

Flood Location, Management and Solution(FLMS): A Flood Predi...

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3rd international conference on Advanced Computing Technologies and applications, ICACTA 2023

作者： Naidu, Siddhanth Pandey, Ambuj Patel, Shiab Wagaskar, Kalpita Tripathy, Amiya Kumar Sen, Sumit Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India Indian Institute of Technology Bombay Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Flooding is a common problem in Kurla, a suburban area of Mumbai, India, with significant impacts on infrastructure, public health, and their livelihoods. This paper proposes FLMS, a Flood Location, Management, and Solution system designed to predict flood events in Kurla to assist in managing them. The FLMS system uses historical data on rainfall gathered from different sources, water levels, and other factors to develop a prediction model based on machine learning techniques. FLMS being a web based application will allow its users to view alerts, contact the respective personnel and create awareness about the different stages of a disaster and ways to limit the losses caused by floods. © 2023 IEEE.

关键词： machine learning

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Prediction of gender from structural MRI images using Multiscale ShuffleNet Extreme learning machine 3

Prediction of gender from structural MRI images using Multis...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Stanley, Berakhah.F. Jeen Retna Kumar, R. Gnanaprakash, V. Bini Palas, P. Patturose, J Gold Beaulah Joel Devadass Daniel, D.J. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRM Easwari Engineering College Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350372120

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MRI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme learning machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme learning machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MRI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare. © 2024 IEEE.

关键词： Magnetic resonance imaging

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