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检索条件"主题词=Machine Learning Algorithms"

共 25782 条记录，以下是471-480 订阅

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Noise Reduction of ECG using Chebyshev filter and Classification using machine learning algorithms

Noise Reduction of ECG using Chebyshev filter and Classifica...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Prakash, M. Bhanu Sowmya, V Gopalakrishnan, E. A. Soman, K. P. Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Computat Engn & Networking Coimbatore Tamil Nadu India

ISBN: (纸本)9781728185293

Cardiac disease detection is a tedious process. Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. The most important factor that limits the detection of cardiac disease is the rare availability of instances of the abnormal condition collected using ECG sensors. And if the signals contain noise, then the classification might become a challenging task. In this work, we address the problem of cardiac disease detection when the dataset has less number of noisy ECG sensor signals. Here, Chebyshev Type II filter and Chebyshev function, which is termed as Chebfun, are used. The Chebyshev filter is used for high-frequency noise removal and Chebfun is used to approximate the signal with its coefficients. These coefficients known as Chebfun coefficients are used as the features. These features are used for classification. In the proposed work, machine learning algorithms, like SVM, logistic regression, decision tree, and AdaBoost, are used for classifying the features extracted from Chebfun.

关键词： Ecg Noise Filtering machine learning algorithms Classification Chebfun Chebyshev Type II Filter

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Fraud Detection and Prevention Using machine learning algorithms: A Review 7

Fraud Detection and Prevention Using Machine Learning Algori...

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7th International Conference on Electrical Energy Systems (ICEES)

作者： Priya, G. Jaculine Saradha, S. VELS Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9781728176123

Digital Fraud has become a threat across all the sectors. Its pivotal for any organization now to have a dedicated focus to detect and prevent fraud and increase their focus on Security. Digitization has revolutionized the way we perform our day to day transactions with a click of a button. On the flip slide, it has also opened up threats through bad actors who can misuse the missing controls in digital apps and thereby impersonate themselves as real customers and perform costly transactions on their behalf resulting in financial losses. Organizations will have to pay attention as it also impacts its brand value. Organizations have learnt their lessons from the past to detect the fraudulent activities in real time by using multitude of factors like using complex algorithms trying to detect fraud patterns. However, fraudsters are also getting intelligent day by day and it requires continuous focus to prevent frauds and to stay ahead of the fraudsters. It is important to monitor key patterns that might help differentiate a real vs fraud transaction. Capturing Customer information like Geo location, authentication, session, device IP address can be maintained. machine learning and application of Artificial Intelligence will play an important part in learning and detecting fraud patterns automatically.

关键词： Fraudulent transactions monitoring authentication and real -time decision making machine learning algorithms cyber security artificial intelligence

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A Tomato Fruit Biomass Prediction Model for Aquaponics System Using machine learning algorithms 7th

A Tomato Fruit Biomass Prediction Model for Aquaponics Syste...

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7th International Conference on Advances in Control and Optimization of Dynamical Systems (ACODS)

作者： Debroy, Pragnaleena Seban, Lalu Natl Inst Technol Silchar Dept Elect & Instrumentat Engn Silchar Assam India

Fruit biomass prediction is a foremost practice to ensure an enhanced production rate and profit maximization. This paper proposes two prediction models for tomato biomass estimation of the aquaponics system using the Artificial Neural Network (ANN) and its hybrid with fuzzy logic i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS). The Feed-forward back propagation network is used in the ANN model and in the ANFIS model, the Gaussian membership function is employed with Sugeno Fuzzy Inference System. The performance evaluation indicated that ANFIS model had attained the best prediction accuracy in terms of MAE of 0.1079 and RMSE of 0.4582, and R-2 of 0.9918 in comparison with the conventional ANN model. This prediction model can help predict the tomato fruit growth of the aquaponics system simply and cost-effectively, supporting farmers in avoiding imbalances in market supply and demand, and economic management. Copyright (C) 2022 The Authors.

关键词： Aquaponics System Tomato fruit Biomass Estimation machine learning algorithms Artificial Neural Network (ANN) Adaptive Neuro-Fuzzy Inference System (ANFIS)

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machine learning algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)

Machine Learning Algorithms for Stroke Risk Prediction Lever...

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

作者： Ogochukwu Ugbomeh Versse Yiye Ebuka Ibeke Chinedu Pascal Ezenkwu Vandana Sharma Ahmed Alkhayyat School of Creative and Cultural Business Robert Gordon University Aberdeen United Kingdom School of Computing Engrg. & Tech Robert Gordon University Aberdeen United Kingdom Computer Science Department Christ University India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I’m Five (ELI5). With the interpretations, healthcare providers can gain insight into patients’ stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system.

关键词： Measurement machine learning algorithms Explainable AI Prevention and mitigation Medical services learning (artificial intelligence) Predictive models Prediction algorithms Informatics Random forests

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machine learning algorithms for Forecasting COVID 19 Confirmed Cases in America

Machine Learning Algorithms for Forecasting COVID 19 Confirm...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Mario Fernando Jojoa Acosta Begoña Garcia-Zapirain University of Deusto

ISBN: (纸本)9781665415897;9781665446839

This paper presents a Multilayer Perceptron and Support Vector machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country's pandemic.

关键词： Support vector machines Measurement COVID-19 machine learning algorithms Pandemics Signal processing algorithms Tools

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machine learning algorithms for Robust Software Vulnerability Detection

Machine Learning Algorithms for Robust Software Vulnerabilit...

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Innovation and Novelty in Engineering and Technology (INNOVA), International Conference on

作者： Gayatri Bajantri Noorullah Shariff C Department of Computer Science & Engineering BLDEA's V.P. Dr. P.G. Halakatti College of Engineering Vijayapur Karnataka India Department of AIML Ballari Institute of Technology and Management Bellary Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Software flaws pose a serious risk to the dependability and security of computer systems and applications in a variety of industries. Because they take a long time and are prone to errors, traditional techniques of finding these vulnerabilities-like human code reviews and static analysis tools-are frequently insufficient. In order to improve the precision, effectiveness, and scalability of vulnerability detection, this paper presents a thorough technique for creating a software vulnerability detection tool that uses machine learning (ML) methods. The tool uses machine learning (ML) to automatically evaluate code samples, find possible security holes, and give developers useful information. The suggested approach consists of multiple phases, such as gathering and preprocessing data, extracting features, training and assessing models, and integrating and deploying tools. To maximise detection performance, a variety of machine learning (ML) algorithms are investigated, such as logistic regression, decision trees, random forests, support vector machines, and deep learning models. The tool's ability to adjust to new threats is guaranteed by its feedback mechanisms and ongoing monitoring. With the help of this cutting-edge technique, software engineers and security experts will be able to detect and address flaws more effectively and automatically, strengthening software systems' defences against ever-changing cyberattacks.

关键词： Training Deep learning Logistic regression Codes machine learning algorithms Feature extraction Software systems Computer crime Random forests Monitoring

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machine learning algorithms for Smart Healthcare: Breast Cancer Prediction

Machine Learning Algorithms for Smart Healthcare: Breast Can...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Manoj Kumar Usha Urmila Pilania Narapureddy Durga Prasad Reddy Kodati Sai Teja Computer Science and Engineering Manav Rachna Univeristy Faridabad India Applied Sciences MRIIRS Faridabad India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Background: Breast cancer continues to be a significant concern for women worldwide, emphasizing the crucial role of early detection in improving treatment outcomes. The machine learning techniques play an important role in early detection of breast cancer. Detection of breast cancer with traditional techniques is time-consuming and does not provide accurate results. So to enhance the accuracy of detection and to decrease time taken, an automatic breast cancer technique is required. This work introduced innovative machine-learning algorithms for the detection of breast cancer. machine learning algorithms used in this paper are Neural Networks (NN), Logistic Regression (LR), Support Vector machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) with a widespread set of related parameters. The dataset taken for the work contains many patient demographics, clinical details, and histopathological information to train and validate machine learning algorithms. For the evaluation of the work accuracy and loss are calculated. The Logistic Regression outperformed with remarkable accuracy 97.4%, over the mentioned five other algorithms. All five models are compared to verify and validate the experimental results. The proposed work requires very less human intervention and attained high accuracy in considerable time. In future, for validation and its applications in clinical settings, the author can design a hybrid technique that can handle real-time clinical data.

关键词： Support vector machines Radio frequency Logistic regression Accuracy machine learning algorithms Artificial neural networks Nearest neighbor methods Breast cancer Real-time systems Decision trees

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machine learning algorithms in context of intrusion detection

Machine learning algorithms in context of intrusion detectio...

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International Conference on Computer and Information Sciences (ICCOINS)

作者： Tahir Mehmood Helmi B. Md Rais University Teknologi Tronoh Petronas Malaysia University Teknologi Petronas Seri Iskandar Perak Malaysia

ISBN: (纸本)9781509025503

Design of efficient, accurate, and low complexity intrusion detection system is a challenging task. Intrusion detection method is a core of intrusion detection system and it can be either signature based or anomaly based. Although, signature based has high detection rate but it cannot detect novel attacks. Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. Many machine learning techniques have been developed to cope with this problem. These machine learning algorithms develop a detection model in a training phase. This paper compares different supervised algorithms for the anomaly-based detection technique. The algorithms have been applied on the KDD99 dataset, which is the benchmark dataset used for anomaly-based detection technique. The result shows that not a single algorithm has a high detection rate for each class of KDD99 dataset. The performance measures used in this comparison are true positive rate, false positive rate, and precision.

关键词： machine learning algorithms Intrusion detection Classification algorithms Support vector machines Computers Decision trees Testing

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machine learning algorithms for predicting electrical performance of perovskite solar cells

Machine learning algorithms for predicting electrical perfor...

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Technology and Engineering Management Society (TEMSCON LATAM), IEEE

作者： Carlos Castañeda Camilo Hernández Sergio Castro Byron Medina Oriana López Jorge Gómez Erick Reyes Alexander Sepúlveda Facultad de Ingenieria Universidad Francisco de Paula Santander Cúcuta Colombia Facultad de Ingenieria Universidad del Magdalena Santa Marta Colombia Facultad de Ingenieria Instituto Metropolitano de Medellín Medellín Colombia Facultad de Ingenieria Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350354317

ISBN: (纸本)9798350354324

Perovskite solar cells (PSCs) have undergone significant evolution in a short time, achieving an efficiency increase of approximately 22% in less than 15 years. These advancements have been driven by empirical findings from experimental cells, leading to increased costs and labor in the development of this technology. Therefore, based on these empirical findings with experimental cells, predictions close to reality can be obtained using machine learning regression algorithms. These models seek complex patterns in a series of variables that aim to describe the cell. This paper aims to compare machine learning algorithms to predict the electrical performance of a PSC using variables such as open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (PCE), based on variables classified as molecular structure, cell layers, and manufacturing processes. The methodology comprises variable selection, feature engineering, model selection, and model evaluation. For the electrical variables, a mean absolute percentage error (MAPE) of 9.3% and a mean absolute error (MAE) of 0.05 V are predicted for Voc; a MAPE of 10.4% and a MAE of 1.5 mA/cm 2 for Jsc; a MAPE of 10% and a MAE of 0.05 for FF. It is concluded that it is feasible to implement machine learning models to predict the electrical performance of PSCs. This integration represents an advancement in renewable energy technology by providing a predictive model that reduces reliance on costly and labor-intensive experimental procedures. The results contribute to the fields of renewable energy and machine learning by offering a tool that can lead to improved efficiency and cost-effectiveness in PSC development.

关键词： Renewable energy sources Manufacturing processes machine learning algorithms Photovoltaic cells Voltage Predictive models Prediction algorithms Approximation algorithms Perovskites Random forests

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machine learning algorithms Application for Fixed-Income Market Analysis: Cross Countries Comparisons

Machine Learning Algorithms Application for Fixed-Income Mar...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Natalia Chernova Olena Serhiienko Maryna Mashchenko Iryna Lisna Olha Haponenko Department of Software Engineering and Management Intelligent Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Business Trade and Logistics Department National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Business School University of Southampton Southampton United Kingdom

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

The research aim is to distinguish the global fixed income market structure and dynamics basing on such core indicator as yields to maturity of government bonds. The processes taken part within or linked with fixed income market are of core importance for the global world economic system as a whole, thus have to be analyzed and researched constantly to ensure decision-making processes on all levels of hierarchy. The following tasks have been implemented to achieve the aim: initial observation set of yields data for world government bonds of different maturities has been formed; initial dataset dimension has been reduced based on principal components analysis technique; fixed-income market structure has been analyzed in the obtained three-dimensional level-slope-curvature space for each researched country; set of countries has been classified onto homogenous groups in multi-dimensional space basing on k-means algorithm; the resulting groupings composition and structure have been analyzed.

关键词： machine learning algorithms Heuristic algorithms Government Decision making Benchmark testing Prediction algorithms Trajectory Information technology Elbow Principal component analysis

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