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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是831-840 订阅

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Smart Attendance Tracking System Using Various machine learning algorithms

Smart Attendance Tracking System Using Various Machine Learn...

引用

Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Saravanan. M.S Faiyaz Ahmad R.T. Thivya Lakshmi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The major goal of this study is to predict the effectiveness of maintaining day-to-day activities of attendant data by using a unique Support Vector machines (SVM) method to monitor the faces. Materials and Methods: Novel SVM algorithm with sample size = 42 and Convolutional Neural Networks (CNN) with sample size = 42 were validated many times to forecast efficiency percentage. Novel SVM algorithm makes use of face vectors to recognize countenance (face) data. Results and Discussions: The recognition of countenance data by monitoring faces performs more efficiently using SVM (Support Vector machine) algorithm with mean efficiency 92.65% and comparing Convolutional Neural Networks with mean efficiency 79.02%. The Support Vector machine algorithm performed significantly better than the Convolutional Neural Networks algorithm is 0.001 (p<0.005). Conclusion: Novel SVM algorithm performs more efficiently than the CNN algorithm to predict correct attendance tracking using countenance images.

关键词： Support vector machines machine learning algorithms Face recognition Manuals Prediction algorithms Vectors Convolutional neural networks Computational complexity Faces Monitoring

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Forecasting Airline Customer Satisfaction through the Application of Diverse machine learning algorithms

Forecasting Airline Customer Satisfaction through the Applic...

引用

Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Ashwin Kumar Vatsal Mehta Trupthi Rao Ashwini Kodipalli Department of AI&DS Global Academy of Technology Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Airline customer satisfaction encapsulates passengers’ overall happiness with an airline’s services, spanning aspects like booking, in-flight experience, and customer service. It is a crucial metric for airline success, influencing brand loyalty and reputation. Achieving and maintaining high customer satisfaction is integral to airlines’ competitiveness and long-term viability in the dynamic aviation industry. This study examines a dataset comprising 129,880 customer records within the airline industry, subjecting it to evaluation by machine learning models, including logistic regression, decision tree, and random forest. Among these, the random forest model stands out, yielding the most favorable results with an accuracy exceeding $94 \%$. This outcome emphasizes the efficacy of employing advanced machine learning techniques for precise predictions in the domain of airline customer satisfaction.

关键词： Measurement Logistic regression machine learning algorithms Atmospheric modeling Customer satisfaction Forestry Forecasting

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Predicting Health Insurance Claim Amount through machine learning algorithms

Predicting Health Insurance Claim Amount through Machine Lea...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Kasarapu Ramani Siliveri Tharun Kumar Powrohitham Pavan Sai Datta Ponne Jamuna K Sai Nithin Dept of Data Science Sree Vidyanikethan Engineering College Tirupati India Department of CSSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Using medical cost data, this paper seeks to predict the cost of health insurance claim amount. The aim is to create an appropriate machine learning model for predicting insurance claim amount and calculate individuals' out-of-pocket expenses. This study proposes machine learning approaches to predict health insurance claim amount through Random Forest Regression, Multiple Linear Regression, XG Boost Regression, Gradient Boosting Regression, and Decision Tree Regression and the experimental results have demonstrated efficacy in making accurate predictions. Random Forest has a remarkable with 96.7% accuracy compared with other models.

关键词： Costs Accuracy machine learning algorithms Linear regression Insurance Forestry Predictive models

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Enhancing Power Grid Efficiency using machine learning algorithms

Enhancing Power Grid Efficiency using Machine Learning Algor...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Saadaldeen Rashid Ahmed Leena Ali Ali Jabbar Hussein Bourair Al-Attar Brwa Abdulrahman Abubaker Jamal Fadhil Tawfeq Abadal-Salam T. Hussain Pritesh Shah Computer Science Department Bayan University Erbil Kurdistan Iraq Department of Medical Physics Al-Ma’moon University College Al-Washash / Baghdad Iraq Al Hikma University College Baghdad Iraq College of Medicin University of Al-Ameed Karbala Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Kirkuk Iraq Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

This study tackles the important real-world topic of boosting power grid efficiency and reliability through the application of machine learning methods. Despite major developments in power grid management, issues exist in properly identifying events and improving system operations. Prior research has shown inadequacies in conventional procedures, driving our inquiry into innovative ways. We give a complete study employing a curated dataset encompassing power system assault scenarios and implementing several machine learning methods, including support vector machines (SVM), recurrent neural networks (RNN), decision trees (DT), and artificial neural networks (ANN). Our results reveal tremendous gains, with SVM reaching the greatest performance measures, including accuracy, precision, recall, F1-score, and ROC-AUC. This research adds to resolving current gaps in power grid optimization approaches, delivering practical insights for enhancing system resilience and efficiency in real-world applications.

关键词： Support vector machines Recurrent neural networks machine learning algorithms Systems operation Artificial neural networks Power grids Power system reliability

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Restaurant Recommendation System using machine learning algorithms

Restaurant Recommendation System using Machine Learning Algo...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Shefali Goyal Nongmeikapam Thoiba Singh Iesh Bajetha Sukhpreet Singh Himanshu Singh Prashant Kumar Department of Computer Science And Engineering Chandigarh University Punjab India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The necessity for recommendation systems spans across various domains, not least in the realm of dining and exploring new locales. Restaurants, in particular, benefit from such systems to both attract customers and offer them a chance to indulge in preferred and renowned dishes. Discovering these dishes, especially in unfamiliar areas, poses a significant challenge. This paper introduces a restaurant recommendation system that leverages food and service rating distributions to calculate matrix density. Additionally, we implement a popularity-based model to suggest restaurants to patrons. A ranking mechanism is utilized, based on scoring, to recommend top-rated restaurants and their most popular dishes. We use singular value decomposition and collaborative filtering to improve the model. The model’s performance is assessed using the RMSE metric. This study is conducted using data from Kaggle and includes the development of a web-based application created with Python’s Flask framework.

关键词： Measurement Knowledge engineering machine learning algorithms Communication systems Collaborative filtering Filtering algorithms Information filters

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A Comparative Analysis of machine learning algorithms for Healthcare Applications

A Comparative Analysis of Machine Learning Algorithms for He...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： B. Vishnu Prabha K. Manikanda Kumaran S. Manikandan S. Palani Murugan Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam India Department of Artificial Intelligence and Data Science E.G.S. Pillay Engineering College Nagapattinam India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Healthcare has increasingly embraced machine learning (ML) algorithms, which have been shown to enhance diagnostic accuracy, treatment effectiveness, and ultimately improve patient outcomes. Despite the diversity of medical data and application requirements, it is still difficult to determine which algorithm will work best for a given healthcare task. The focus of this paper is on comparing various medical laboratory (ML) algorithms that are commonly used in the healthcare domain. The performance of algorithms is evaluated across a range von der Gesundheitswesen Aufgabenstellungen (disease forecasting, medical imaging analysis, treatment recommendation), and are then used to evaluate them using publicly available datasets. The efficacy of algorithms is measured using performance metrics like accuracy, sensitivity (for some algorithms even faster than the Precise Lotion Function), specificity and area under the receiver operating characteristic curve (AUC-ROC) Our analysis provides insight into the strengths and limitations of each algorithm and helps healthcare professionals and researchers choose algorithms for specific healthcare applications.

关键词： machine learning algorithms Accuracy Sensitivity Transfer learning Clustering algorithms Medical services Receivers Forecasting Medical diagnostic imaging Diseases

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Exploring Quantum machine learning algorithms for Enhanced Data Classification and Clustering in Complex Systems - Integration with Gradient Boosting and K-means for Improved Performance

Exploring Quantum Machine Learning Algorithms for Enhanced D...

引用

Cybernation and Computation (CYBERCOM), International Conference on

作者： M. Babu M. Arun S. Pushparani A. Muthukrishnan N. Poongavanam Department of Information Technology Rajalakshmi Engineering Çollege Thandalam Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning.

关键词： Support vector machines Adaptation models Quantum computing machine learning algorithms Accuracy Computational modeling Boosting Vectors Optimization Standards

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Stochastic Large-scale machine learning algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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IEEE International Conference on Big Data

作者： Biyi Fang Diego Klabjan Truong Vo Dept. of Engineering and Applied Mathematics Northwestern University Evanston IL USA Dept. of Industrial Engineering and Management Sciences Northwestern University Evanston IL USA Dept. of Machine Learning and Data Science Northwestern University Evanston IL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm.

关键词： machine learning algorithms Computational modeling Stochastic processes Optimization methods machine learning Predictive models Prediction algorithms Sparks Distributed computing Convergence

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Cloud Computing Security: A Comprehensive Review of machine learning algorithms

Cloud Computing Security: A Comprehensive Review of Machine ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Zaid Alsalami Mohammed Salim Imad Al-Din Hussein Al-Saidi H.H.Alaak Reem Hamdan Khaddour Nabaa Kareem Taaban Zahraa Naji Abdel Amir Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Al-Farahidi University Baghdad Iraq Computer Engineer Department Al-Kunooze University College Basra Iraq The University of Mashreq Baghdad Iraq Mazaya University College Thi-Qar Iraq Business Administration Department Kut University College Al Kut Wasit Iraq Uruk University Baghdad Iraq

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Utilizing the cloud implies having organized assets, particularly information and administration, accessible at whatever point you would like them without requiring uncommon client courses of action. More as of late, CC has developed into a arrange of both open and private datacenters advertising clients a single online course of action. By putting preparing and information capacity closer to the end clients, edge computing may be a forward-thinking approach to computers that guarantees to speed up reaction times and spare transfer speed. Versatile phone applications are dispersed by means of versatile gadgets that give scattered computing. By the by, security concerns with CC and edge computing, such deciding client defenselessness and organization together declaration, make it troublesome to rapidly distinguish compute models. machine learning is the consider of computational processes on computers that truly gotten to be way better with involvement. In this investigate extend, we offer an examination of CC security dangers, issues, and strategies that utilize one or more ML calculations. Fortification, semi-supervised, uncontrolled, and synchronized learning are among the obvious machine learning computations that are surveyed in arrange to fathom cloud security issues. Following, we compare the points of interest, drawbacks, and focuses of intrigued of each methodology to decide how viably it was executed. Furthermore, we select to investigate up and coming ponders centered on secure CC models. Since cloud computing offers unmatched adaptability, adaptability, and cost-efficiency, it has totally changed how businesses store, process, and oversee information. But this fast acknowledgment has moreover brought up genuine security issues, requiring solid and intelligent security strategies to defend basic information. With the improvement of advanced strategies for danger location, inconsistency recognizable proof, and interruption avoidance, machine learning (ML) a

关键词： Computers machine learning algorithms Cloud computing security Computational modeling machine learning Organizations Security Virtualization Unsupervised learning Edge computing

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Enhanced Detection of Evil Twin Attacks in Public Wi-Fi Networks Using machine learning algorithms

Enhanced Detection of Evil Twin Attacks in Public Wi-Fi Netw...

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International Conference on Information Technology Research (ICITR)

作者： J.D.A.S.K. Nanayakkara M.M.P.R.M. Bandara M.S. Mawjood R.A.P.M. Perera Kavinga Yapa Deemantha Siriwardana Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

The widespread use of public Wi-Fi networks has significantly increased users' exposure to cyber threats, with Evil Twin attacks posing a particularly insidious risk. These attacks manipulate users into connecting to fraudulent Wi-Fi access points, enabling attackers to intercept sensitive data or execute further malicious actions. This research introduces a robust, machine learning-driven methodology for the real-time detection of Evil Twin attacks in public Wi-Fi environments. Utilizing the AWID2 dataset, the study applied advanced feature selection and preprocessing techniques, including data balancing, noise reduction, and dimensionality reduction, to ensure data quality and model relevance. The resulting dataset was refined from 153 to 24 essential features, enhancing model performance and effi-ciency. Multiple machine-learning classifiers, including Random Forest, K-Nearest Neighbors, and Naive Bayes, were evaluated, with the Random Forest algorithm achieving a notably high accuracy of 99.9186%. These findings validate the framework's efficacy in real-time Evil Twin detection, providing a practical and effective solution to strengthen public Wi-Fi security and minimize cybersecurity risks for users and organizations.

关键词： machine learning algorithms Noise reduction Organizations Nearest neighbor methods Feature extraction Real-time systems Network intrusion Information technology Wireless fidelity Random forests

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