检索结果-内蒙古大学图书馆

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： G Shinde Sarika K. G. Kharade R.K. Kamat Department of Computer Science Shivaji University Kolhapur Department of Electronics Shivaji University Kolhapur Dr.Homi Bhabha State University Mumbai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The development of bettered machine learning algorithms is the primary focus of this exploration, with the ultimate thing of enhancing the delicacy of heart complaint opinion. presently available individual styles constantly warrant the necessary delicacy, which can affect treatments that are moreover delayed or incorrect. The purpose of this exploration is to develop models that can relate and prognosticate heart complaints from complex medical data with lesser perfection. These models will be created by utilizing advanced ways in machine learning, similar to deep learning and ensemble styles. An expansive quantum of data preprocessing, point selection, and model training are all part of the exploration process. These conditioning are carried out on large datasets attained from healthcare institutions. The primary findings suggest that there has been a substantial improvement in individual delicacy in comparison to the conventional styles, along with an increase in both perceptivity and particularity. Not only does this approach demonstrate the eventuality of machine learning to revolutionize healthcare diagnostics, but it also holds the pledge of perfecting patient issues by allowing for earlier and more accurate opinions. Eventually, the findings of this study could contribute to the development of individual tools that are more reliable, effective, and scalable, which would eventually lead to bettered operation and treatment of heart complaints.

关键词： Heart Deep learning Training Ethics machine learning algorithms Soft sensors Sociology

来源：评论

学校读者我要写书评

暂无评论

Application Research of machine learning algorithms in Big Data Mining and Prediction

Application Research of Machine Learning Algorithms in Big D...

引用

Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Hao Wang School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

Student information mining plays a significant role in student management, yet existing information management methods are relatively outdated. machine learning algorithms have the capability to predict future trends based on historical data. Whether it's sales forecasting, stock price prediction, weather forecasting, or disease spread prediction, machine learning algorithms can leverage big data for training, thereby enhancing prediction accuracy and reliability. This paper proposes the use of machine learning algorithms for clustering analysis of student information. Currently, the LSTM algorithm suffers from runtime issues. Hence, we propose improvements to the LSTM algorithm. We introduce the Grey Wolf algorithm into the existing algorithm to construct one with fast convergence speed and high prediction accuracy. Applying the improved algorithm to student information clustering, experimental results demonstrate a significant improvement in the accuracy of student information identification.

关键词： Training machine learning algorithms Simulation Clustering algorithms Weather forecasting Big Data Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms

引用

BIODATA MINING 2023年第1期16卷 2-2页

作者： Appiahene, Peter Asare, Justice Williams Donkoh, Emmanuel Timmy Dimauro, Giovanni Maglietta, Rosalia Univ Energy & Nat Resources Dept Comp Sci & Informat Sunyani Ghana Univ Energy & Nat Resources Dept Basic & Appl Biol Sunyani Ghana Univ Bari Aldo Moro Coordinatore Consiglio Interclasse Corsi Studio In Bari Italy CNR Inst Intelligent Ind Syst & Technol Adv Mfg Bari Italy

BackgroundAnemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood *** method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised;Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models *** results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naive Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting *** invasive method of detecting anemia is expensive and time-consuming;however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naive Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the machine learning Algorit

关键词： Anemia Image Augmentation machine learning algorithms Red blood cell Palpable palm Region of Interest Non-invasive

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach for Usability Evaluation of learning Management Systems Using machine learning algorithms

A Hybrid Approach for Usability Evaluation of Learning Manag...

引用

Frontiers in Education (FIE) Conference

作者： Richard Torres-Molina Mohammed Seyam Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research full paper describes a hybrid approach based on data, questionnaire answers, and machine learning algorithms to predict usability scores in learning Management Systems (LMSs) to improve student learning and satisfaction. Students need to achieve their learning goals by interacting with LMSs. To attain these goals, usability evaluation ensures effectiveness (task completion), efficiency (time measurement), and satisfaction (positive attitude). Usability evaluation usually follows questionnaires, user testing of the LMS, and expert reviews. Although these methods are widely used due to several benefits, they face challenges related to trying these software systems multiple times until the system satisfies student needs, human subjectivity perception, and lack of software system adaptability. We propose this hybrid approach to face these challenges, promote student engagement with the system, and create a better design in the LMS courses. The aim is to identify features extracted from the LMS to predict usability scores with machine learning techniques. We evaluated this strategy through a case study with data collected from undergraduate students at a public university in the United States. The students' tasks were answering a quiz, posting in a forum, and uploading an assignment. These activities in the LMS allow the extraction of ten features into the machine learning algorithms. These attributes are time quiz, time forum, time assignment, grade quiz, word count message post, file size, file type, clicks module quiz, clicks module forum, and clicks module assignment. The four targets are from scores of the System Usability Scale and UseLearn questionnaires. Random Forest produces the best performance of average mean square error and root mean square error among machine learning algorithms. The results are promising, though there are alternatives for improvements. Our proposed approach contributes to the engineering and computing education field by prov

关键词： learning management systems Visualization machine learning algorithms Feature extraction Software systems Time measurement Usability Random forests Faces Testing

来源：评论

学校读者我要写书评

暂无评论

Estimating natural soil drainage classes in the Wisconsin till plain of the Midwestern USA based on lidar derived terrain indices: Evaluating prediction accuracy of multinomial logistic regression and machine learning algorithms

引用

GEODERMA REGIONAL 2023年 35卷

作者： Rahmani, Shams R. Libohova, Zamir Ackerson, Jason P. Schulze, Darrell G. Purdue Univ Dept Agron 915 Mitch Daniels Blvd W Lafayette IN 47907 USA Dale Bumpers Small Farms Res Ctr United States Dept Agr Agr Res Serv 6883 South State Highway 23 Booneville AR 72927 USA Soil Hlth Inst 2803 Slater Rd Suite 115 Morrisville NC 27560 USA

Accurate spatial prediction of natural soil drainage condition is not only important for agriculture and hydro-logical modeling but also for installing subsurface drainage and onsite waste disposal systems. For this research, 154 sites were selected based on a stratified random sampling method. For each site, drainage class was identified based on visual examination of soil cores. A digital elevation model developed from lidar data was used to derive seven terrain indices. Terrain indices were used to predict drainage class using four prediction models: multinomial logistic regression and three machine learning algorithms (random forest, C5.0, and artificial neural network). Based on 30% random hold-back validation data, all digital soil mapping (DSM) models provided similar results. The artificial neural network provided relatively higher overall accuracy of 70%, a kappa coefficient of 0.59, and a Brier score of 0.34. The rest of the DSM models as well as SSURGO provided an accuracy that ranged between 64 and 66%, a kappa coefficient that ranged between 0.52 and 0.54, and a Brier score that ranged between 0.41 and 0.46.

关键词： Alfisols Natural soil drainage class machine learning algorithms Mollisols Multinomial logistic regression C5.0 decision tree model Random forest Artificial neural network Digital soil mapping (DSM) SSURGO lidar digital elevation model (DEM) Terrain attributes Stratified random sampling

来源：评论

学校读者我要写书评

暂无评论

Accuracy comparison of machine learning algorithms at various wear-locations for activity identification post stroke: A pilot analysis 43

Accuracy comparison of machine learning algorithms at variou...

引用

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Veerubhotla, Akhila Ehrenberg, Naphtaly Ibironke, Oluwaseun Pilkar, Rakesh Kessler Fdn W Orange NJ 07052 USA Rutgers New Jersey Med Sch Newark NJ 07103 USA

ISBN: (纸本)9781728111797

Objective and accurate activity identification of physical activities in everyday life is an important aspect in assessing the impact of various post-stroke rehabilitation therapies and interventions. Since post-stroke hemiparesis affects gait and balance in individuals with stroke, activity identification algorithms that consider stroke-specific movement irregularities are needed. While wearable physical activity monitors provide the means to detect activities in the free-living, algorithms using their data are specific to the wear location of the device. This pilot study builds, validates, and compares three machine learning algorithms (linear support vector machine, Random Forest, and RUSBoosted trees) at three popular wear locations (wrist, waist, and ankle) to identify and accurately distinguish mobility-related activities (sitting, standing and walking) in individuals with chronic stroke. A total of 102 minutes of data from two lab visits of three-stroke participants was used to build the classifiers. A 5-fold cross-validation technique was used to validate and compare the accuracy of classifiers. RUSBoosted trees using data from waist and ankle activity monitors, with an accuracy of 99.1%, outperformed other classifiers in detecting three activities of interest.

关键词： Wrist Support vector machines Legged locomotion machine learning algorithms Medical treatment Biology Object recognition

来源：评论

学校读者我要写书评

暂无评论

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable machine learning algorithms 43

Enhanced Critical Congenital Cardiac Disease Screening by Co...

引用

43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Lai, Zhengfeng Vadlaputi, Pranjali Tancredi, Daniel J. Garg, Meena Koppel, Robert, I Goodman, Mera Hogan, Whitnee Cresalia, Nicole Juergensen, Stephan Manalo, Erlinda Lakshminrusimha, Satyan Chuah, Chen-Nee Siefkes, Heather Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Univ Calif Davis Dept Pediat Sacramento CA USA UCLA David Geffen Sch Med Los Angeles CA 90095 USA Cohen Childrens Med Ctr New York NY USA Univ Utah Hlth Salt Lake City UT USA Univ Calif San Francisco San Francisco CA 94143 USA Sutter Hlth Sacramento CA USA

ISBN: (纸本)9781728111797

Critical Congenital Heart Disease (CCHD) screening that only uses oxygen saturation (SpO2), measured by pulse oximetry, fails to detect an estimated 900 US newborns annually. The addition of other pulse oximetry features such as perfusion index (PIx), heart rate, pulse delay and photoplethysmography characteristics may improve detection of CCHD, especially those with systemic blood flow obstruction such as Coarctation of the Aorta (CoA). To comprehensively study the most relevant features associated with CCHD, we investigated interpretable machine learning (ML) algorithms by using Recursive Feature Elimination (RFE) to identify an optimal subset of features. We then incorporated the trained ML models into the current SpO2-alone screening algorithm. Our proposed enhanced CCHD screening system, which adds the ML model, improved sensitivity by approximately 10 percentage points compared to the current standard SpO2-alone method with minimal to no impact on specificity.

关键词： Pediatrics machine learning algorithms Sensitivity Pulse measurements machine learning Approximation algorithms Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Predictive Performance of machine learning algorithms Trained with Sparse Data

Predictive Performance of Machine Learning Algorithms Traine...

引用

IEEE Aerospace Conference (AeroConf)

作者： Dewey, H. Heath DeVries, Derek R. Northrop Grumman 9160 N Hwy 83 Corinne UT 84307 USA

ISBN: (纸本)9781728174365

In the field of prognostic health management of aerospace assets and fleets, accurate prediction of the future state of an asset based on current and historical data has always been a challenge. Several different methods of computing that future state are commonly utilized including physics-based simulation and machine learning algorithms. Physics-based simulations have the advantage of true predictability in that they are able to evolve a model of the system using first principles based on causality and predict future states that have yet to be observed on real assets, however they also have the disadvantage of being difficult, time-consuming, and expensive to construct. machine learning algorithms are the reverse: simple, fast, and cheap to construct but without true predictability since they can only fit input data to the data sets with which they were trained. For some aerospace fleets with a significant amount of historical degradation and failure data to train a model, machine learning algorithms provide very good predictions, but for other fleets with little historical degradation and failure data, machine learning may not provide good predictions of the future states of the assets. This paper explores the accuracy of the predictions made with machine learning algorithms when they are trained with sparse data with varying quality. An empirical study is performed where machine learning models are trained with data representing various historical data set types that are typically encountered in aerospace health management (e.g. low quality dense data, high quality sparse data, etc.), and the results from running those algorithms are compared with an idealized physics-based model. The predictive performance of the machine learning algorithms are quantified, and suggestions on when it is appropriate to utilize machine learning algorithms in aerospace health management systems are presented.

关键词： Degradation Rockets machine learning algorithms Computational modeling machine learning Predictive models Aerospace testing

来源：评论

学校读者我要写书评

暂无评论

Comparison of machine learning algorithms for Classification of Partial Discharge Signals in Medium Voltage Components 11

Comparison of Machine Learning Algorithms for Classification...

引用

11th IEEE-PES Innovative Smart Grid Technologies Europe (IEEE-PES ISGT Europe)

作者： Kumar, Haresh Shafiq, Muhammad Hussain, Ghulam Amjad Kauhaniemi, Kimmo Univ Vaasa Sch Technol & Innovat Vaasa Finland Tallinn Univ Technol Dept Elect Power Engn & Mechatron Tallinn Estonia Amer Univ Kuwait Dept Elect & Comp Engn Salmiya Kuwait

ISBN: (纸本)9781665448758

Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. machine learning algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs);accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

关键词： electrical insulation partial discharge features extraction machine learning algorithms classification key performance indicators

来源：评论

学校读者我要写书评

暂无评论

Recent innovation in benchmark rates (BMR):evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

引用

Financial Innovation 2021年第1期7卷 942-961页

作者： Öer Depren Mustafa Tevfik Kartal Serpil KılıçDepren YapıKredi Bank IstanbulTurkey Borsaİstanbul Financial Reporting and Subsidiaries Directorate Reşitpaşa Mahallesi Borsaİstanbul CaddesiNo:434467 IstanbulTurkey Department of Statistics Yildiz Technical UniversityIstanbulTurkey

Some countries have announced national benchmark rates,while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of *** that Turkey announced the Turkish Lira Overnight Reference Interest Rate(TLREF),this study examines the determinants of *** this context,three global determinants,five country-level macroeconomic determinants,and the COVID-19 pandemic are considered by using daily data between December 28,2018,and December 31,2020,by performing machine learning algorithms and Ordinary Least *** empirical results show that(1)the most significant determinant is the amount of securities bought by Central Banks;(2)country-level macroeconomic factors have a higher impact whereas global factors are less important,and the pandemic does not have a significant effect;(3)Random Forest is the most accurate prediction *** action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

关键词： Benchmark rate Determinants machine learning algorithms Turkey

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：