检索结果-内蒙古大学图书馆

Impact of random Forest and XGBoost Algorithms on Improving Patient Outcomes Compared to Standard Decision-Making Methods in Healthcare Predictive Analytics

Impact of Random Forest and XGBoost Algorithms on Improving ...

引用

2024 Inter.a.ional Conference on Cyber.a.ion and Computation, CYBErCOM 2024

作者： Senthilvadivu, S. ramesh, P.S. Narang, Sudha Praveena, N. Shakila, J. Sudha, I. Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE TamilNadu Chennai India Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Jeppiaar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering SIMATS University Tamil Nadu Chennai India

ISBN: (纸本)9798331542344

In the field of healthcare, it is impor.a.t to bring quick, accurate and prompt predictions in order to serve the needs of patients better, avoid re-hospitalizations, and better.a.locate resources. With this, this paper proposes an ML framework for enhancing decision-making in healthcare predictive analytics employing random Forest and XGBoost algorithms. The first and major problem solved is the inefficiency of the existing decision-making appr.a.hes which is caused by the impossibility to analyze and process big and big complex data. The proposed solution uses these machine learning models for.a.tivities such as estimating mor.a.ity, critical illness, r.a.mission, and length of stay using massive healthcare data set. Due to the utilization of sophisticated mechanisms of machine learning, the models provide much more precise predictions to r.a.h better results in terms of early interventions and the usage of resources. The outcomes reveal that random Forest and XGBoost improve the prediction accuracy and XGBoost can improve most of the measures compared with random Forest. The findings of the models suggest their usefulness for providing daily operational decision making in clinical contexts, helping enhance the delivery of care and management of resources in healthcare facilities. © 2024 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Smart r.a. Damage Survey System Using Machine Vision and IoT technology 3

Smart Road Damage Survey System Using Machine Vision and IoT...

引用

3rd Inter.a.ional Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Mutijar.a. Kusprasapta Alunjati, Figo Agil Hidayat, Fadhil Kurnia, ranna Arsyad, Zulkifli School of Electrical Engineering and Informatics Bandung Indonesia National Institute of Technology Department of Civil Engineering Bandung Indonesia Bandung State Polytechnic Department of Computer and Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

r.a. is an essential transpor.a.ion infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the r.a. sur.a.e. A good-condition r.a. ensures the land traffic runs well. This research is a collaboration between the Bandung institute of technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for r.a. damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the perfor.a.ce of the r.a. survey activities. The previous r.a. damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect r.a. defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer.a.chitecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the r.a. condition image automatically, and a dashboard application to reveal the r.a. condition on map. The perfor.a.ce resulted in accuracy, precision, recall, and F-score of the r.a. damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Analysis of data from UAVs for surveillance and thr.a. identification in maritime ar.a. 8

Analysis of data from UAVs for surveillance and threat ident...

引用

8th IEEE/ACIS Inter.a.ional Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime ar.a.. The concept and the aim is to assist the national maritime surveillance authorities in the identification of tr.a.s and the recognition of the exact type of the tr.a.. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based appr.a.hes and Deep Learning-based appr.a.hes providing more infor.a.ion about the video or.a. image than traditional appr.a.hes to recognition. These appr.a.hes are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, r-CNN, Fast r-CNN, Faster r-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or.a. image capture. An experimental study was conducted in r.a.-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

引用

remote Sensing in Earth Systems Sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most impor.a.t aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most impor.a.t things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be gr.a.ly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and nor.a.isation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with var.a.ional fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer.a.large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzer.a.d AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

来源：评论

学校读者我要写书评

暂无评论

Integrated nor.a. discriminant analysis in mapreduce for diabetic chronic disease prediction using bivar.a.t deep neural networks

引用

Inter.a.ional Jour.a. of Infor.a.ion technology (Singapore) 2024年第8期16卷 4915-4929页

作者： ramani, r. Dhinakaran, D. Edwin raja, S. Thiyagarajan, M. Selvaraj, D. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Nor.a. Discriminant Feature Selection based regressive Deep Neural Mapreduce (NDFS-rDNMr) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-rDNMr is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Nor.a. Discriminative Preprocessing Model (NDPM) and bivar.a.t regressive deep artificial neural network with Mapreduce (Br.a.NMr) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-rDNMr conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional for.a.s via parameter rescaling to fit within predefined value ranges. Min–max nor.a.ization is applied to improve system accuracy while preserving data relationships. The Br.a.NMr classifier utilizes bivar.a.t regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar.a.tivation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection Mapreduce Nor.a.ization regression analysis

来源：评论

学校读者我要写书评

暂无评论

Predictive Management of EV Charging Stations Using Machine Learning 10

Predictive Management of EV Charging Stations Using Machine ...

引用

10th Inter.a.ional Conference on Electrical Energy Systems, ICEES 2024

作者： Alex David, S. Begum, Almas Hemalatha, D. Senthil, K. Monikaa, r. ruth Naveena, N. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798350353778

Based on experimental results the predictive accuracy of random Forest, Decision Tree, and XGBoost was much higher than the Linear regression algorithm in EV Charging Management using machine learning models. random Forest handled the charging station dynamics well, and the complex data relationships were managed easily with XGBoost. On the other hand, Linear regression performed better because results displayed high errors and its model fit numbers were not significant. In contrast, the Decision Tree has glaring issues with overfitting and generalization. The results highlight the impor.a.ce of advanced data science methods for efficient EV infrastructure management by yielding exact forecasts about demand and energy consumption behaviors. Future endeavors will continue to build upon these models, incr.a.ing the scale of analysis and providing state-of-the-art solutions towards developing an appr.a.h for both scalable development as well as a push toward more sustainable transpor.a.ion with improved operational efficiencies and reliability within EV networks. © 2024 IEEE.

关键词： Adver.a.ial machine learning

来源：评论

学校读者我要写书评

暂无评论

Generative Adver.a.ial Networks (GANs) for.a.tistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

引用

2024 Inter.a.ional Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adver.a.ial Networks (GANs) a big advance. Style transfer converts semantic visual content into cr.a.ive styles. recently, GANs for style transfer have gained popularity. Adver.a.ial training of GANs to cr.a.e r.a.istic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adver.a.ial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's cr.a.ive qualities. By studying adver.a.ial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend ar.a. for improvement. The experiments show that DE-GAN cr.a.es r.a.istic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adver.a.ial networks

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Appr.a.h for the Identification and Classification of Cardiovascular Diseases Using ECG Signals and Sensor data

A Machine Learning Approach for the Identification and Class...

引用

2024 Inter.a.ional Conference on Smart Devices, ICSD 2024

作者： Shaik, Nazeer Dharani, M. Kumar, Bura Vijay Gupta, Vikas Singh, Jagendra Pal, Yogesh SRIT-Autonomous Department of Computer Science Engineering Anantapur India K S R Institute for Engineering and Technology School of Electronics and Communication Engineering Tiruchengode India SR University School of Computer Science and Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350394412

Cardiovascular diseases (CVDs) continue to be a major concern in the medical field to date. Among the many diagnostic tools, electrocardiogram (ECG) remains one of the main ways with which to detect cardiac abnor.a.ities. Although classic ECG interpretation is powerful, it is a laborious and time-demanding task and tends to be onerous for physicians. This study suggests a means of effective machine learning to incr.a.e the accuracy and speed of diagnosing CVDs based on ECG signals and other sensor data. On the other hand, we are concerned with the application of Frequency Domain Measures in deep-learning methodologies of image classifiers. Other models include the raw ECG sent to a 1-dimensional convolutional model together with the XGBoost model for the time-series feature-based prediction. Preliminary assessment reveals that Frequency Domain Measures provide good atr.a. fibrillation prediction but are quite limited for other types of arrhythmias. Computational results for the XGBoost model are, however, efficient for predictions on long data but proceed at the cost of extended inference times, considering the computational demand of the pre-processing stage. This paper opens up the scope of the potential for machine learning in revolutionizing ECG-based CVD detection, and we have to keep on the protocols of refinement and research toward this usage. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

A review on GIF-Based Steganography Techniques 5th

A Review on GIF-Based Steganography Techniques

引用

5th Inter.a.ional Conference on recent Innovations in Computing, ICrIC 2022

作者： Chola, Abhinav Awasthi, Lalit Kumar Singh, Samayveer Department of Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789811998751

Data privacy has been a significant concern since the beginning of the Internet, and in the current scenario, the need and impor.a.ce of data privacy are incr.a.ing exponentially. Today, a multitude of media for.a.s including text, images, videos, Graphics Interchange For.a. (GIFs), etc., are available for exchanging data, and techniques of cryptography and steganography play a key role in maintaining data secrecy during such transmissions. This paper elaborates on the GIF-based techniques of steganography. It provides a detailed comparison of the working, advantages and limitations of the major.a.pr.a.hes developed in this field in the last three decades. It provides a clear.a.d chronological understanding regarding the evolution of GIF-based steganography methods and discusses future directions to enhance the existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

AI-Progressions in Automated Facial Paralysis Evaluation: A Comprehensive Survey of Traditional to Deep Learning Appr.a.hes 4

AI-Progressions in Automated Facial Paralysis Evaluation: A ...

引用

4th Inter.a.ional Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiruthiga, S. Anbuananth, C. Dhilipkumar, V. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331523923

Deep learning mechanisms allow computers to solve complex r.a.-time problems with complex neural networks, it employs a vital role in health sector, for.a.sisting the medical practitioners with quick and accurate decision making. Various studies on this research ar.a.proves that deep learning appr.a.hes are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain str.a.. Accurate and timely detection is most impor.a.t and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the r.a.-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for.a.curate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：