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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4741-4750 订阅

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Exploring Diverse machine learning Approaches for Coronary CT Images Analysis: A Comparative Study

Exploring Diverse Machine Learning Approaches for Coronary C...

引用

IEEE URUCON

作者： Santiago Villafuerte Francisco J. Alvarez-Padilla Vladimir Trujillo-Olaya José F. Valencia Dept. of Ciencias y Tecnologías de la información Universidad de San Buenaventura Cali Colombia Dept. of Bioingeniería Traslacional Universidad de Guadalajara Guadalajara México

ISBN: (数字)9798350355383

ISBN: (纸本)9798350355390

This study evaluates the performance of various machine learning algorithms on coronary computed tomography angiography (CCTA) images for detecting coronary atherosclerosis. The algorithms include Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), Support Vector machines (SVM), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Convolutional Neural Network with Transfer learning (CNN w/ TL). Performance metrics such as Accuracy, Recall, F1-Score, and Area Under the Curve (AUC) are used to compare their effectiveness on separate validation and test datasets. The results highlight $\mathrm{CNN} \mathbf{w / T L}$ as the most effective algorithm, achieving an AUC of $\mathbf{0. 9 1}$ on the validation dataset. The CNN models also perform well, with all metrics over 0.80. Random Forest (RF) demonstrates robust performance with metrics at 0.75 (Validation) and 0.70 (Test).

关键词： Measurement Support vector machines Radio frequency machine learning algorithms Recurrent neural networks Computed tomography Angiography Atherosclerosis Nearest neighbor methods Convolutional neural networks

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Data-Driven Facility Location Balancing using machine learning and Shannon Entropy

Data-Driven Facility Location Balancing using Machine Learni...

引用

Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Mohammad Amir Dastgheib Hamzeh Beyranvand Jawad A. Salehi Department of Electrical Engineering Optical Networks Research Lab (ONRL) Sharif Quantum Center (SQC) Sharif University of Technology Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering Sharif Quantum Center (SQC) Institute for Convergence Science & Technology (ICST) Sharif University of Technology Tehran Iran

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

This paper presents a novel approach to data-driven facility location balancing that leverages the power of machine learning (ML) and Shannon entropy. We introduce a ML algorithm that, for the first time, employs nearest neighbor methods for stochastic gradient estimation to learn continuous-space facility locations from data. This innovative approach achieves a speedup of two orders of magnitude over traditional methods. Evaluated through a proposed adjusted entropy ratio metric, our method improves load balancing by $\mathbf{6 2 \%}$ compared to k -means, particularly in densely populated areas where k- means falls short. Shannon Entropy ensures balanced coverage in facility location tasks, offering significant benefits in scenarios requiring more equitable subdivision of crowded regions.

关键词： Measurement Maximum likelihood estimation machine learning algorithms Accuracy Shape machine learning Nearest neighbor methods Load management Entropy Optimization

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Relay Selection machine learning-Based for a DF Cooperative System with Energy Harvesting and Signal Space Diversity

Relay Selection Machine Learning-Based for a DF Cooperative ...

引用

Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Ahmed Ammar Ahmed Oun William Martell M. Ajmal Khan Ohio Northern University Ohio University

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

machine learning techniques have been employed in wireless communication systems to offer resilient and low-complexity solutions. Accordingly, this paper explores the utilization of machine learning algorithms for real-time relay selection in a cooperative communication system with signal space diversity and multiple energy harvesting relays. The assumed decoding scheme at the relays is decode-and-forward (DF), and the relay selection criteria involve successful decoding from the source, sufficient energy, and the best channel to the destination. Con-ventional machine learning algorithms, including Decision Tree (DR), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector machines (SVM), are implemented, and the algorithm with the highest accuracy is utilized for real time relay selection. Performance evaluation is in terms of outage probability, and the exact outage probability performance is de-termined through Monte Carlo simulation. Results indicate that KNN exhibits the highest accuracy; nevertheless, its performance falls short of closely approximating the exact outage probability due to the complexity of the data, necessitating the need for more advanced classification models such as artificial neural networks or deep neural networks to accurately approximate the exact performance of the proposed system.

关键词： Support vector machines machine learning algorithms Spatial diversity Artificial neural networks Nearest neighbor methods Probability Real-time systems

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Face Recognition Approach Using the Deep learning Techniques

Face Recognition Approach Using the Deep Learning Techniques

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Redwan Al-Dilami Ammer T. Zahary Abobakr Al-Sharafi Osamah Al-Anashi Suliman Maresh Faculty of Computing and TT University of Science and Technology Sana'a Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Facial recognition technology has emerged as a pivotal tool in both security and technical domains. Its adoption has accelerated in several developed nations, where it finds applications across various sectors. For instance, institutions and businesses leverage this technology to monitor employees and detect unauthorized individuals, while banks utilize it to verify identities during cash withdrawals, serving as an alternative to traditional ATM cards. In this context, our research aims to propose a machine learning-based method for improving facial recognition accuracy. We conducted a thorough review of existing algorithms in the field and identified Support Vector machine (SVM) and Principal Component Analysis (PCA) as the most effective based on prior studies. To evaluate these algorithms, we compared SVM and PCA in the context of identity verification. Our experiments involved training the algorithms on a dataset comprising 5,749 facial images. The results demonstrated that the SVM algorithm significantly outperformed PCA in terms of accuracy. We believe that the successful application of the SVM algorithm can provide substantial benefits to institutions looking to enhance their security measures through reliable facial recognition systems.

关键词： Support vector machines Training Accuracy machine learning algorithms Reviews Face recognition Safety Security Reliability Principal component analysis

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Improving Surgical Security using Artificial Intelligence and Deep learning

Improving Surgical Security using Artificial Intelligence an...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Rakeem Shaikh Daksh Gupta S Malini Computer Science Specialization IOT Vellore Institute of Technology Vellore India Computer Science Vellore Institute of Technology Vellore India SCOPE Vellore Institute of Technology Vellore India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The safety of the patient in the different stages of a surgical procedure is an important aspect as concerns risks that need to be catered for as well to avert untoward effects. This research considers Artificial Intelligence and machine learning technologies as a core mechanism that can be used to augment the surgical safety systems already in place that determine surgical patients’ risk of death while hospitalized and to also ameliorate pre-operative safety checks. The first approach focuses on traditional checklists by proposing an automated voice validation system of checklist items that uses the Gemini Application Programming Interface (API) on python. This voice validation seeks to mitigate the coverage of human error in relation to the notice of the absence or overweight of the required documents. The second approach describes how machine learning algorithms are used to model a surgical risk predictor across wide bases of patients’ directories, past medical history, and lab tests. Categorical values were made engineering values, and patient were standardized of age with focus on elective surgeries only since emergency surgeries are excluded. For missing values, mean and median imputation was used and PCA for dimensionality reduction. To address the class imbalance issue, the Synthetic Minority Over-sampling Technique (SMOTE) was employed. For the fast and effective binary outcome classification, Support Vector machine Classification (SVC) with hyperparameters optimized through the GridSearchCV. The concomitant use of speech recognition for checklist verification and ML methods to assess postoperative risk can significantly improve safety during surgical procedures, making it possible to entirely change the scope of preoperative procedures and patient organizational aspects of hospitals.

关键词： machine learning algorithms Surgery Support vector machine classification Static VAr compensators Speech recognition Predictive models Security Application programming interfaces Python Principal component analysis

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A Comparative Analysis for Loan Approval Prediction using machine learning

A Comparative Analysis for Loan Approval Prediction using Ma...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Akash Badhan Abhishek Rana Simarjit Singh Malhi Parampreet Kaur Bishal Saha School of Computer Science & Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The financial sector of any nation is the key factor in elevating the economy of nation. The biggest issue that finances departments are facing is to approve loans for valid candidates only. Our proposed model for predicting the loan approval for valid candidates uses three different supervised learning algorithms for classification of loan approved or not, on the basis of candidate attribute values. In proposed study ensemble technique has been used to find out best classifier algorithm to train our model. In this paper we are comparing KNN, Decision Tree, Logistic Regression, SVC, and Naïve Bayes classifier algorithms based on their accuracy for provided data set to find out the highly accurate and efficient algorithm for designing a model for approving loan to valid candidate which will also help in minimizing the financial frauds.

关键词： machine learning algorithms Accuracy Supervised learning Static VAr compensators Predictive models Nearest neighbor methods Prediction algorithms Classification algorithms Mobile applications Random forests

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Research on the Construction Method and Application of Personalized Learner Portraits Based on machine learning

Research on the Construction Method and Application of Perso...

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Information and Communication Technology (ICTech), International Conference of

作者： Yun Yang Lulu Zhang Siyang Li Changchun Institute of Education Changchun China Mei He Kou Shi No.5 Middle School Meihekou China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

With the rapid development of educational technology, personalized learning has become a key way to improve educational effectiveness. This study uses machine learning technology to construct learner portraits and applies it to the optimization of personalized recommendation systems and teaching strategies. Through detailed experimental design and rigorous data analysis, the research shows that personalized learner portraits can effectively improve the accuracy of recommendation systems and the level of personalization of teaching content. The study adopted a variety of data preprocessing and feature engineering techniques to ensure data quality and feature effectiveness. In addition, by comparing the performance of different machine learning algorithms, this study determined the optimal model, implemented it in actual teaching scenarios, and evaluated the effect of the system through online experiments and user surveys. The results show that personalized recommendations significantly improve learner engagement and learning effectiveness. Although the research has achieved certain results, there are still problems such as the scope of data collection and sample size limitations. Future research can expand the data set, adopt advanced algorithms, and deeply explore the broad application of learner profiling in teaching practice.

关键词： Surveys machine learning algorithms Data integrity Data preprocessing machine learning Educational technology Data collection Information and communication technology Recommender systems Optimization

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Enhancing Vehicle Routing with Time Windows: A machine learning-Driven Ant Colony Optimization Approach

Enhancing Vehicle Routing with Time Windows: A Machine Learn...

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Frontiers of Information Technology (FIT)

作者： Khizer Tariq Ali Awais Safdar Mubashir Zaman Syed Usman Ali Shah School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging optimization problem in logistics and transportation, where the objective is to efficiently plan routes for vehicles to service a set of customers within specified time windows while minimizing costs. This paper introduces a novel approach to solving VRPTW using a machine learning-Driven Ant Colony Optimization (ML-ACO) algorithm, referred to as machine learning-Driven Ant Colony Optimization for Vehicle Routing with Time Windows (ML-ACO-VRPTW). The proposed algorithm enhances traditional ACO by integrating a machine learning model, to predict heuristic values that capture the urgency of time windows and vehicle capacity constraints. The algorithm begins with initializing pheromone levels and parameters, followed by constructing routes based on probabilistic path selection influenced by pheromone intensity and machine learning-predicted heuristic values. Key innovations include dynamic pheromone updates that adapt to the algorithm's progress and the incorporation of penalties for time window violations and vehicle capacity exceedances. The pheromones are used to maintain a memory of the paths, and the machine learning model ensures adaptive and accurate heuristic predictions.

关键词： Ant colony optimization Adaptation models machine learning algorithms Heuristic algorithms Vehicle routing Transportation machine learning Predictive models Prediction algorithms Vehicle dynamics

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Comparative study on Loan Approval Prediction Utilizing machine learning Approaches

Comparative study on Loan Approval Prediction Utilizing Mach...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Tiyas Sarkar Manik Rakhra Vikrant Sharma Aryan Maan Amanpreet Singh Gagandeep Singh Cheema School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India School of Computer Science & Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science and Applications Lovely Professional University Phagwara Punjab India Mittal School Of Business Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Due to the recent advancements in the banking sector and the growing popularity of borrowing money, a large number of people are submitting applications for bank loans. However, the growing incidence of loan defaults is a major problem for the banking sector in this vibrant economy. As a result, it is becoming increasingly challenging for banking authorities to accurately evaluate loan requests and manage the risk of loan default. In the banking sector, the two most important questions are (i) How hazardous is the borrower? and (ii) Should we provide the borrower money given the risk? In order to overcome the above-described problems, this study presents a pair of machine learning algorithms that assess particular features in order to decide whether or not to offer a loan. This will help banking authorities select the best individuals from a pool of applications. This study provides a thorough evaluation that contrasts the two algorithms: Decision Trees and Random Forests. Both approaches were applied to the same dataset, and the outcomes showed that the method using Random Forests outperformed the algorithm known as Decision Tree by a wide margin. In the end, it appears that a random forest approach performs better than the Decision tree. DT and RF, with a $\mathbf{9 0 \%}$ confidence level, Between the two most widely used machine learning algorithms and two statistically insignificant groups, the unbiased sample T-test result of $\mathbf{p}=0.33$ ($\mathbf{p}\gt0.05$) shows a statistically significant agreement. Random Forests seem to be able to forecast loan acceptance more accurately than Decision Trees.

关键词： Accuracy machine learning algorithms Training data Banking Prediction algorithms Classification algorithms Decision trees Random forests Regression tree analysis Testing

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Analyzing the Interpretability of machine learning Prediction on Student Performance Using SHapley Additive exPlanations

Analyzing the Interpretability of Machine Learning Predictio...

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IEEE International Conference on Teaching, Assessment and learning for Engineering (TALE)

作者： Wan Chong Choi Chan-Tong Lam António José Mendes Faculty of Applied Sciences Macao Polytechnic University China CISUC University of Coimbra Coimbra Portugal Dep. of Informatics Engineering CISUC University of Coimbra Coimbra Portugal

ISBN: (数字)9798350376234

ISBN: (纸本)9798350376241

This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable machine learning (EML) techniques to enhance interpretability. A range of algorithms, including Random Forest, Extra Trees, CatBoost, XGBoost, Naive Bayes, and KNearest Neighbors (KNN), were compared, with Extra Trees delivering the best results. Distinct from other EDM research mainly focused on predictive efficiency, we contributed by using the EML technique of SHapley Additive Explanations (SHAP), rooted in the Game Theory framework, to enhance model interpretability at both global and individual levels. At the global level, summary plots showed overall feature impacts, bar plots quantified the average effect of each feature, and dependence plots highlighted specific relationships. At the individual level, force plots identified critical features for individual predictions, decision plots traced the cumulative impact of features from the base value to the final output, and waterfall plots provided a breakdown of predictions. This study contributes to EDM by offering accurate predictive models and detailed interpretability, helping educational stakeholders make data-informed decisions to improve student outcomes.

关键词： machine learning algorithms Additives Force Predictive models Nearest neighbor methods Prediction algorithms Stakeholders Random forests Programming profession Bars

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