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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Enhancing Smart Healthcare Services Through Deep learning-Based Medical data Analysis

Enhancing Smart Healthcare Services Through Deep Learning-Ba...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Sheik Jamil Ahmed Saira Banu Atham Hajar Kadhim Deepak Gupta Aaied Eqab Muraay Majed M. K. Mohamed Faizal Department of CSE Madanapalle Institute of Technology & Science Andhra Pradesh India Presidency School of Computer Science and Engineering Presidency University Bangalore Karnataka India Department of Medical Laboratory Techniques Uruk University Baghdad Iraq Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Medical Engineering Al Kitab University Kirkuk Iraq Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In order to enhance patient care and medical decision-making, smart healthcare services are progressively utilizing cutting-edge technologies. Deep learning (DL), one of these technologies, has become a potent instrument for evaluating enormous volumes of medical data, allowing for improved clinical procedures and more precise diagnosis. This study focuses on enhancing smart healthcare services through deep learning-based medical data analysis, with a particular emphasis on Named Entity recognition (NER) and Relation Extraction (RE) tasks from clinical records. The study is implemented using Python software on the MIMIC-III dataset, utilizing an optimized Particle Swarm Optimization (PSO)-BioBERT model. The model achieved remarkable performance in both NER and RE tasks, with accuracy rates of 99.2% for NER and 99.5% for RE, and F1-scores of 98.1% and 98.8%, respectively. Compared to traditional models like Bi-LSTM and BERT, the PSO-BioBERT model significantly outperformed them, with the proposed model exceeding Bi-LSTM by 6.2% in accuracy and BERT by 12.12%. The findings highlight that the PSO-BioBERT model provides substantial improvements in extracting and classifying medical entities and their relationships from clinical data. These developments revolutionize smart healthcare services by making it possible to analyze unstructured medical information more accurately and consistently. The study emphasizes the effectiveness of deep learning models, particularly PSO-BioBERT, in improving data-driven decision-making in healthcare, which could ultimately enhance patient care outcomes and healthcare system efficiency. Future work could focus on integrating these models with real-time clinical systems for broader applications, including predictive analytics and personalized treatment plans. This approach holds promise for revolutionizing how healthcare data is utilized to improve overall patient care.

关键词： Deep learning data analysis Accuracy Decision making Medical services Software Real-time systems data mining Medical diagnostic imaging Python

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The Soul Safety

The Soul Safety

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Asha Rani Borah B V Santosh Krishna Shrinidhi.M. H Deepika Neelappa Gouda Prafulla Poojary Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

This Sexual assault has been one of the serious problem across the globe. A lot of people, especially women go through this but unfortunately not many will be able to open up to the public about such incidents. The main goal of this research is to suggest a strategy for integrating machine learning with various algorithms to improve the classification of the many forms of assault actions and construct harassment detection systems. This study offers a view for the better understanding of sexual assault and to detect it with text mining, qualitative, and temporal trend analysis methods that are efficient.

关键词： Text mining machine learning algorithms Computational modeling Employment Psychology Market research data models

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Design of Intelligent Pest Monitoring System Based on Image Classification Algorithm 3

Design of Intelligent Pest Monitoring System Based on Image ...

引用

3rd international conference on Control and Robots (ICCR)

作者： Yu Lina Yue Xiuming Shandong Xiehe Univ Coll Comp Jinan Peoples R China

ISBN: (数字)9781728175621

ISBN: (纸本)9781728175621

The emergence of pattern recognition, artificial intelligence and machine learning. Bring new height to the development of smart agriculture. This paper studies an image classification algorithm, An intelligent pest monitoring system is built based on this algorithm. The system adopts UAV monitoring equipment carrying artificial intelligence module and intelligent recognition algorithm and ground monitoring equipment. Realize diseases and insect pests accurate prediction and intelligent diagnosis. To solve the current industry monitoring mode is single, low level of intelligence, monitoring type limitations and other issues. With the power of wisdom, drive China's agriculture to flourish.

关键词： image classification machine learning diseases and insect pests monitor

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Authentication using Biometric data from Mobile Cloud Computing in Smart Cities

Authentication using Biometric Data from Mobile Cloud Comput...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Artificial Intelligence Engineering Dept AI and Robotics Institute Near Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITAM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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Two approaches to machine learning classification of time series based on recurrence plots 3

Two approaches to machine learning classification of time se...

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3rd IEEE international conference on data Stream mining and Processing, DSMP 2020

作者： Kirichenko, Lyudmyla Radivilova, Tamara Bulakh, Vitalii Zinchenko, Petro Saif Alghawli, Abed Kharkiv National University of Radio Electronics Department of Applied Mathematics Kharkiv Ukraine Kharkiv National University of Radio Electronics V.V. Popovskyy Dept. of Infocommunication Engineering Kharkiv Ukraine Prince Sattam Bin Abdulaziz University Department of Computer Science Aflaj Saudi Arabia

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132143

The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machine learning methods were considered: in the first case, quantitative fractal and recurrent characteristics of the time series were classification features, in the second case, image recognition of recurrence plots was carried out. The results showed fairly high classification quality for both methods, and relative advantage of the image classification method. © 2020 IEEE.

关键词： Time series

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TCN-HBP: A deep learning method for identifying hormone-binding proteins from amino acid sequences based on a temporal convolution neural network 3

TCN-HBP: A deep learning method for identifying hormone-bind...

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2021 3rd international conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Guo, Jing School of Electrical and Information Engineering Tianjin University Tianjin China

Hormone-binding proteins (HBPs) are carrier proteins that specifically bind to targeted hormones. Some evidence suggests that the abnormal expression of HBPs causes various diseases. Therefore, it is significant to accurately identify HBPs to study these diseases. Recently, many researchers have proposed traditional machine learning methods to complete this work, but these methods are neither suitable for training on large-scale datasets nor take into account the contextual features of HBPs. In this paper, I propose a new deep learning method, TCN-HBP, to distinguish HBPs. TCN-HBP consists of a coding layer, embedding layer, convolutional neural network (CNN) layer and temporal convolutional network (TCN) layer. The coding and embedding layers extend the protein sequences into two-dimensional matrix data. The CNN layer convolves the matrix data to form feature maps. The TCN layer captures the contextual features present in the feature maps. Experiments show that the data generalization capabilities and recognition accuracy (99.15%) of TCN-HBP on large datasets perform better than previous methods. © Journal of Physics: conference Series 2021.

关键词： Embeddings

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Analyze the impact of the epidemic on New York taxis by machine learning algorithms and recommendations for optimal prediction algorithms 2021

Analyze the impact of the epidemic on New York taxis by mach...

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3rd international conference on Robotics Systems and Automation Engineering, RSAE 2021

作者： Liu, Zheng Xia, Xinjing Zhang, Haipeng Xie, Zihui College of Engineering Tongji University Shang Hai200092 China College of Letters and Science University of California DavisCA95616 United States College of Arts and Sciences Boston University BostonMA02215 United States Department of Electrical Engineering and Computer Science The Pennsylvania State University State CollegePA16803 United States

ISBN: (纸本)9781450388467

With the growth of the economy and population, the transportation infrastructure of cities faces various challenges such as lacks of parking spots and traffic jams. One of the alternatives is to take public transport combined with proper utilization of demand-responsive transport such as Taxi or Uber. Therefore, it is valuable to improve the efficiency of these responsive transport. This research evaluates the predicting performance of different prediction models such as GBDT, XGBoost, and Random forest on taxi trip duration. At the same time, new elements were added to the modified data to contribute a higher accuracy rate including the snow status and precipitation data. Moreover, exploratory data analysis and data mining was conducted taking concerns of the Covid-19 effect of the year 2020. © 2021 ACM.

关键词： Decision trees

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Autoencoder-Lstm Framework for Accurate Foreign Exchange Volatility Prediction

Autoencoder-Lstm Framework for Accurate Foreign Exchange Vol...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Parijat Saurabh Malik Bader Alazzam Nagarajan S D. Harikishan Reddy Sakar Fatah Sulaiman B Kiran Bala Department of Economics Mahatma Gandhi Kashi Vidyapith Varanasi India Faculty of Information Technology Jadara University Irbid Jordan School of Management Studies Bannari Amman Institute of Technology Erode Tamilnadu India Department of Management Studies Sri Venkateswara College of Engineering (Autonomous) Andhra Pradesh India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Accurately predicting foreign exchange volatility is crucial for financial institutions, traders, and policymakers. This makes it an extremely complex and dynamic market, hence failing traditional methods of prediction for capturing intricate patterns present in time-series data. Advanced machine learning models, have shown great potential in addressing this challenge. We propose an Autoencoder-Lstm model that leverages the strengths of both autoencoder for feature extraction and LSTM networks for time-series forecasting. These networks have gated mechanisms controlling the flow of information, particularly suitable for financial forecasting in their ability to extract long-term dependencies and trends which improve the forecast's accuracy as far as selective memory in prediction of volatility goes. The autoencoder simplifies input data by extracting important features and dropping out unnecessary noise. After extracting features from the dataset the network processes them efficiently while recognizing past time series connections. The research is implemented using Python software, for model development. The framework is evaluated using real-world foreign exchange data, incorporating both raw price features and derived features such as returns and volatility measures. The model is trained and tested on a dataset of daily forex rates, achieving an impressive prediction accuracy of 99.2%. Performance metrics, including RMSE (0.012), MAE (0.008), and MSE (0.00014), underscore the model's ability to minimize prediction errors, significantly outperforming traditional models. The results demonstrate the Autoencoder-Lstm’s effectiveness in predicting exchange rate volatility, making it a valuable tool for financial forecasting. This model's high accuracy and robustness further establish its potential for broader applications in other time-series prediction tasks.

关键词： Accuracy Autoencoders Time series analysis machine learning Predictive models Feature extraction Software Robustness data mining Forecasting

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Cloud detection of multi-feature remote sensing images based on deep learning 3

Cloud detection of multi-feature remote sensing images based...

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3rd international conference on Environmental Prevention and Pollution Control Technologies, EPPCT 2021

作者： Yao, Jiaqi Zhai, Haoran Wang, Guanghui Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing100048 China College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China College of Resource Environment and Tourism Capital Normal University Beijing100048 China School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou221116 China

Satellite remote sensing technology provides massive image data for meteorological field, which can accurately extract the spatial distribution of cloud, and can be used to analyze the spatial and temporal changes of cloud. Aiming at the problems that the traditional algorithm is sensitive to noise and has poor recognition effect on broken clouds, a multi-feature remote sensing image cloud detection algorithm based on neural network structure is proposed, which has a good recognition effect on all types of clouds. The experimental results show that the accuracy of cloud detection in this paper reaches 92%, which is about 6% higher than the traditional algorithm, and avoids the disadvantage of the traditional algorithm which is sensitive to noise. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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GC-UNET: An Improved Unet Model for Mangrove Segmentation Using Landsat8 2021

GC-UNET: An Improved Unet Model for Mangrove Segmentation Us...

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3rd international conference on Big data Engineering, BDE 2021

作者： Dong, Yichao Yu, Kun Hu, Wenxin School of Data Science Engineering East China Normal University 3663 N. Zhongshan Rd. Shanghai200062 China

ISBN: (纸本)9781450389426

Accurate and effective recognition of mangrove on the remote sensing images is necessary in protecting the mangrove ecosystem. Nowadays, many methods have been developed for remote sensing image segmentation. They can be classified as methods based on geographic and methods based on machine learning. However, these methods may be too time-consuming or not accurate enough in some cases. Due to these factors, we propose GC-UNet, a novel method based on UNet, which embeds Global Context Block to get long-range dependency, uses SPConv to focus on intrinsic information and adopts Adaptively Spatial Feature Fusion to process features from different levels. It greatly strengthens the presentation ability of the model. The experimental results demonstrate that our proposed model obviously enhances the performance of the image segmentation. We compare our method with DeepLabV3 and OCNet. Our method achieves a dice coefficient of 0.8604, exceeding the above popular semantic segmentation models. © 2021 Association for Computing machinery.

关键词： Landsat

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