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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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application of Support Vector machine in Health Assessment of Shipborne Radar Antennas 4

Application of Support Vector Machine in Health Assessment o...

引用

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

作者： Tao, Min Wang, Yu Zhang, Yuxuan China Satellite Maritime Tracking Control Department Jiangyin214431 China

ISBN: (纸本)9798350342710

Shipborne antennas are important equipment to ensure the completion of aerospace measurement ship missions. Due to long-term operation under complex and harsh conditions, key components such as motors, gears, bearings, and other mechanical equipment will inevitably fail, causing serious safety hazards and directly affecting the completion of the mission. this paper analyzes the role and significance of the antenna health management system, introduces the theory of support vector machine, establishes the model and application framework of support vector machine, implements the mathematical model in the antenna health management system, and verifies the feasibility of the model through the antenna measured data validation. © 2023 IEEE.

关键词： Support vector machines

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Systematic Literature Review on Deep learning Techniques in Electronic Health Records 4

Systematic Literature Review on Deep Learning Techniques in ...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Chitra, G. Basha, Syed Muzamil Reva University School of Computer Science and Engineering India

ISBN: (纸本)9798350300093

Nowadays there is a tremendous amount of digital information stored in Electronic Health Records (EHR). the EHR incorporates a wealth of information consisting of clinical notes, patient information, medications, procedures, laboratory test reports and so on. thus, it has also led to huge impetus on exploiting the EHour R to aid clinical decision support systems. there is a growing research body in this direction to develop useful insights from FHR the typical EHR data is irregularly sampled, heterogenous, multi-modal lacking annotations with noisy and missing information. An additional challenge of need for privacy also means very limited publicly available datasetsfor benchmarking. machine learning techniques are at the forefront of this effort in providing tools to analyse the huge amount of heterogeneous data. Recent advances in deep learning techniques have immensely contributed to growing applications using EHR Towards this, overview of recent advancements and techniques employed to analyse EHR data is introduced. A review of the literature and discuss the challenges of different approaches. In addition to that birds-view summary of the methods to aid future research improvements and trigger innovative applications in healthcare. © 2023 IEEE.

关键词： learning systems

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Carnatic Music Identification of Melakarta Ragas through machine and Deep learning using Audio Signal Processing 4

Carnatic Music Identification of Melakarta Ragas through Mac...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Chhetri, Aryan Raj Kumar, Kshitiz Muthyala, Mayank Prashanth Shreyas, M.R. Bangalore, Raghu A. PES University Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350335750

this study focuses on predicting the raga, which is a fundamental aspect of Indian classical music, from a *** file using machine learning and deep learning models. the approach involves audio processing and feature extraction with the Librosa library. Finally, the system is trained and tested on the extracted data to predict the raga. this study has various applications, such as music education, therapy, and synthesis. Predicting the raga of a given audio clip can be challenging, as it requires a deep understanding of the underlying musical structure and nuances of Indian classical music. However, with machine learning and deep learning techniques, it has become possible to develop automated systems that can accurately predict the raga from an audio file. the proposed project focuses on developing such a system using machine learning and deep learning models. the study involves audio processing and feature extraction using the Librosa library, a widely used Python package for music analysis. Librosa provides various tools for audio processing, such as audio loading, time and frequency domain analysis, and feature extraction. © 2023 IEEE.

关键词： Audio signal processing

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Transforming IoT with Federated Multimodal learning: A Privacy-Centric Approach 4

Transforming IoT with Federated Multimodal Learning: A Priva...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Kumar, Mukesh thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350349900

Suppliers face significant challenges when discussing cloud-based health apps, such as implementing highly secure procedures and rigorous privacy safeguards. this applies to the fact that the acceptance of these applications is limited by the need for such quality of service, which is achieved mainly by reducing latency. this work is devoted to tackling these hurdles through the cutting-edge application of edge computing and model-federated learning aspects. Federated learning, a complex and distributed paradigm for machine learning helps perform computations near the data source and overcomes the constraint problem of a distant data source. Integrating the newest edge computing, our system includes an intelligent instrument that is applied to extract clinical data using Clustered Federated learning (CFL). the value of this framework is evaluated using the performance metrics of two benchmark datasets, one of which represents different kinds of problems that occur in the day-to-day life of people, which concludes the effectiveness and significance of implementing machine learning at the edge in the health care sector the presented results above strongly suggest the fact that CFL promotes this model results in improvement. the performance indicators of the CFL model showed an appraisal boost with the increments in X-ray and Ultrasound datasets by 16% and 11%, respectively, when compared to those trained conventionally. Hence, edge AI through CFL can achieve the same tasks, and the possible scenarios can also be remarkably superior compared to a centralized data system. © 2024 IEEE.

关键词： Federated learning

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Research on Deep learning Models Combining Transformer and BERT: application in Network Intrusion Detection 4

Research on Deep Learning Models Combining Transformer and B...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Fang, Zhongqiu Hong, Ziwei School of Information and Electrical Engineering Heilongjiang Bayi Agricultural University Daqing163319 China Mathematical School Renmin University of China Beijing100086 China

ISBN: (纸本)9798331528676

In response to the low recognition accuracy, complex preprocessing techniques, and unstable detection accuracy in network intrusion detection, this paper proposes a deep learning model based on the combination of Transformer and BERT. Firstly, based on the basic theories of Transformer and BERT, an intrusion detection model was established to obtain detailed information on network traffic characteristics, intrusion behavior types, and detection performance. Secondly, in order to eliminate the interference of these issues, a principal component analysis strategy was proposed. Furthermore, based on the characteristics of Transformer neural networks, an intrusion detection model was constructed that comprehensively considers the influence of network traffic characteristics and intrusion behavior types, providing a more accurate description of network intrusion behavior. Finally, an intrusion detection system based on Transformer and BERT was built, and the effectiveness of the proposed algorithm was verified through numerical examples. ©2024 IEEE.

关键词： Adversarial machine learning

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Quantum Algorithms for Shapley Value Calculation 4

Quantum Algorithms for Shapley Value Calculation

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Burge, Iain Barbeau, Michel Garcia-Alfaro, Joaquin School of Computer Science Carleton University Ottawa Canada Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau France

ISBN: (纸本)9798350343236

In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post hoc explanations of machine learning (ML) models. However, this approach does not easily translate to eXplainable Quantum ML (XQML). Finding Shapley values can be highly computationally complex. We propose quantum algorithms which can extract Shapley values within some confidence interval. Our results perform in polynomial time. We demonstrate the validity of each approach under specific examples of cooperative voting games. © 2023 IEEE.

关键词： machine learning

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Price Prediction of Ethereum Using Blockchain Historical and Exchange Data by Supervised machine learning Algorithms 4

Price Prediction of Ethereum Using Blockchain Historical and...

引用

4th international conference on Industrial Engineering and Artificial Intelligence, IEAI 2023

作者： Narang, Harendra Kumar Shrirame, Vishal K. Kurrey, Bhupesh National Institute of Technology Department of Mechanical Engineering CG Raipur India

ISBN: (纸本)9798350324280

Predicting or forecasting crypto currency prices is now one of the most difficult tasks in crypto market trading due to its qualities and dynamic nature. the purpose of the present work is to analyse the exchange and blockchain data and develop a prediction model using machine learning. Ethereum (Eth) is one of the crypto currencies, and data has been taken from the price time series from January 1, 2017 to December 31, 2021, on a daily basis. the algorithm for gathering data has been trained and tested using a machine-learning algorithm. the adequacy of the developed machine learning models was validated using MAPE, RSME, MAE, and R2 scores. the developed model can predict future results with an accuracy of up to 85% for 7 days. Based on the findings, it is suggested that blockchain historical data and exchange data can be utilised as input characteristics in the development of a machine learning model to forecast Ethereum's future price. © 2023 IEEE.

关键词： Forecasting

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Prediction of Weather Forecasting with Long Short-Term Memory using Deep learning 4

Prediction of Weather Forecasting with Long Short-Term Memor...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Karthika, S. Priyanka, T. Indirapriyadharshini, J. Sadesh, S. Rajeshkumar, G. Rajesh Kanna, P. Intelligence and Machine Learning Bannari Amman Institue of Technology Department of Artificial Sathyamangalam India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Velalar College of Engineering and Technology Department of Ai and Ds Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350300888

Weather forecasting has a greater effect on human life, recently this filed has gained a significant research attention. the existing numerical weather prediction models are very complex to solve. therefore, the primary goal of this research work is to create a novel and lightweight weather forecasting model. Here, the time series data have been used from Kaggle website. the obtained dataset is then processed with the help of deep learning techniques. the Long Short-Term Memory (LSTM) algorithm is used to produce better result with more accuracy when compared with other deep learning methodologies. therefore, from the results it is evident that the proposed model can be used to predict the weather conditions with greater accuracy and assist farmers and others who depend on weather. © 2023 IEEE.

关键词： Long short-term memory

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A machine learning-based Automated Approach for Mining Customer Opinion 4

A Machine Learning-based Automated Approach for Mining Custo...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kumar, Anuj Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350300093

Amazon Reviews allows customers to review the purchased or used products. this type of user-generated content is incredibly helpful for e-commerce businesses;whether it is from existing customers or new ones, every review provides a valuable perspective and feedback on a certain product. Amazon product reviews are an incredibly powerful tool for both buyers and businesses in determining the perceived value of a product, informing decisions, and improving customer trust and loyalty. this study explores the use of machine learning-based methods to assess the sentiment of Amazon product reviews from a range of product categories, including electronics, home appliances, and fashion items. through the use of Natural Language Processing (NLP) and deep learning algorithms, this study aims to determine whether there is a correlation between customer sentiment and product category. the analysis will further facilitate the development of a model that can accurately predict sentiment scores for any given batch of reviews or comments. According to the study, using Text Blob, Logistic Regression, SVM, and Multinomial Naive Bayes methods can improve classification task accuracy. It produced 91% accuracy with SVM and Logistic Regression and Multinomial Naive Bayes methods are 84% and 87% accuracy with on 4444 positive reviews and 469 negative reviews. © 2023 IEEE.

关键词： Sentiment analysis

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Comprehensive Analysis of machine and Deep learning Models for Breast Cancer Diagnosis and Risk Assessment with Diverse Datasets 4

Comprehensive Analysis of Machine and Deep Learning Models f...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Dash, Rajon Kohinoor, Md Saidur Rahman Chowdhury, Promise Ghosh Computer Science and Engineering Leading University Sylhet3112 Bangladesh King Fahd University of Petroleum and Minerals Information and Computer Science Dhahran Saudi Arabia

ISBN: (纸本)9798350349900

Breast cancer, a pervasive global health challenge, demands innovative diagnostic approaches for timely intervention and improved outcomes. this study presents an extensive investigation into the application of machine learning (ML) and deep learning (DL) techniques for breast cancer diagnosis and risk assessment. Utilizing the Wisconsin Breast Cancer Database (WBCD) and the NKI Breast Cancer dataset, our study employs a diverse range of ML models, including Support Vector machines, Random Forests, Decision Trees, K-nearest neighbors, Naive Bayes, and Logistic Regression, alongside DL models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). the investigation incorporates comprehensive data preprocessing, feature selection, and 10fold cross-validation to optimize model performance. Results showease the CNN outperformed with 98.24% accuracy on the WBCD dataset, while the Decision Tree model leads on the NKI dataset with 91%. Precision, recall, and F1-Score metrics provide a nuanced evaluation of model efficacy. the study not only addresses the limitations of previous works but also introduces a holistic approach, emphasizing the significance of diverse datasets in enhancing breast cancer diagnostic accuracy that holds promise for impactful advancements in healthcare. the findings pave the way for practical applications, including the development of a userfriendly mobile application for personalized breast cancer detection and risk evaluation to foster personalized and timely interventions. © 2024 IEEE.

关键词： Convolutional neural networks

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