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检索条件"主题词=Machine Learning Algorithms"

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Personalized Shatavari Nutrition Recommendations for Menopausal Women using machine learning algorithms

Personalized Shatavari Nutrition Recommendations for Menopau...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： E Logapriya R Surendran M Soniya P. J Sathish Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering R M.K Engineering College Kavaraipettai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research provides a comprehensive approach in designing a recommendation system for women’s health issues, with emphasis on menopause and optimal eating plan with Shatavari nutrition plan incorporated. The data includes symptom severity; heart rate; temperature; sleep; and food and water intake, which has been obtained from patients’ medical records, structured surveys, the users themselves, and through healthcare apps. Handling of missing values involves the completion of the missing records by forward method and imputation. Kindly find below the steps taken to pre-process the given data set for its numerical and categorical features respectively; Standard Scaler for numerical data set and OneHotEncoder for the binary data set. Variable creation is used to improve the performances of the models by creating new variables from other variables. Raw, selected and vectorized features are used to perform and compare multiple machine learning algorithms such as linear regression, logistic regression, support vector machines, decision random forest and gradient boosting, XGBoost, LightGBM, and CatBoost. The present study reveals that LightGBM has the maximum accuracy of $96 \%$. The proposed system combines collaborative and content-based filtering, which will provide accurate and individual health and lifestyle recommendations for enhancing the quality of women’s life during menopausal transition.

关键词： Support vector machines Heart rate Temperature distribution Logistic regression Accuracy machine learning algorithms Linear regression Medical services Recommender systems Random forests

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An Empirical Analysis of machine learning algorithms for Agricultural Crop Prediction

An Empirical Analysis of Machine Learning Algorithms for Agr...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Ujwal Pai Jakarbet Suhas S P Savithramma R M Ashwini B P Department of CSE Siddaganga Institute of Technology Visvesvaraya Technological University Tumakuru Karnataka India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In recent years, machine learning has rightfully claimed its position across multiple industries, streamlining various aspects of daily life. With the growing need for sustainable agricultural practices, there is a rising request for advanced decision-making machines that can boost crop productivity while reducing resource consumption. This study digs deeper into the use of machine learning algorithms to create an intelligent crop recommendation system. Many researchers globally are focusing on this challenge. By leveraging diverse datasets from Kaggle, covering factors such as soil and climate conditions. This research aims to suggest suitable machine learning model for implementing crop-prediction application which in-turn provide farmers with data-driven insights to make optimal crop selection decisions. Several ML algorithms, including Linear Regression, Naïve Bayes, Lasso Regression, Support Vector Regression (SVR), Decision Tree, Random Forest, K-Nearest Neighbors (k-NN), and Gradient Boosted Regression Trees (GBRT), were assessed for their ability to predict suitable crops based on environmental parameters. The models were trained and validated using Indian climatic conditions-based data. Among these, the Random Forest algorithm performed the best, achieving 96% accuracy based on the R2 score.

关键词： Support vector machines Productivity machine learning algorithms Crops Soil Nearest neighbor methods Prediction algorithms Random forests Regression tree analysis Recommender systems

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Evaluating machine learning algorithms for Early Detection of GDM

Evaluating Machine Learning Algorithms for Early Detection o...

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

作者： Osamah Al-Azzawi Sefer Kurnaz Department of Electrical & Computer Engineering Institute of Graduate Studies Altinbas University Istanbul Turkey

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Through the use of the Gestational Diabetes Mellitus (GDM) Data Set, this research conducts an in-depth analysis of machine learning methods for the early diagnosis of GDM. The effectiveness of various algorithms is evaluated by the use of metrics such as recall, accuracy, precision, and F1 score. There are several algorithms that fall under this category, such as Logistic Regression, Long Short-Term Memory (LSTM), Gradient Boosting Classifier, Random Forest, and K-Nearest Neighbors. It is necessary to do meticulous data preparation in order to find the best models for GDM prediction. Some examples of this preparation include feature scaling and principal component analysis (PCA). Should this research prove to be effective, it has the potential to provide insight on how machine learning may be used to enhance the diagnosis and management of GDM.

关键词： machine learning algorithms Predictive models Prediction algorithms Data models Classification algorithms Reliability Long short term memory

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A Screening Method for Left Ventricular Systolic Dysfunction by Single-Channel Electrocardiogram Using machine learning algorithms

A Screening Method for Left Ventricular Systolic Dysfunction...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Natalia Kuznetsova Alexander Suvorov Petr Chomakhidze Philippe Kopylov I.M. Sechenov First Moscow State Medical University Moscow Russia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Background. Analysis of a single-channel electrocardiogram can potentially be used as a screening method to detect systolic dysfunction of the left ventricle. The purpose of our study was to develop a new screening method for detecting a decrease in systolic function of the left ventricle based on single-channel ECG and pulse wave recording using machine learning methods. Materials and methods. The study prospectively included 1039 patients aged 18 years and above. A transthoracic echocardiographic study and 1-minute single channel electrocardiogram were performed for each patient. Spectral analysis of the electrocardiogram based on the Fourier transform. More than 200 parameters were included in machine learning algorithms. Results. For ejection fraction decrease: Lasso regression showed a sensitivity of 92,2%, specificity of 90,1% (AUC=0.920); Random Forest Classifier sensitivity - 88.2%, specificity - 83,3% (AUC=0.834). Algorithm approbation has shown diagnostic accuracy of 90,1% in left ventricular systolic dysfunction. Conclusions. machine learning models, based on the single lead ECG parameters, as well as age and gender may simplify screening diagnostics of ejection fraction decrease prior to echocardiographic study for in time heart failure diagnostics with high accuracy.

关键词： machine learning algorithms Sensitivity Accuracy Software algorithms Electrocardiography Software Classification algorithms

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Detection of Fake Profiles and News on Social Media Using machine learning algorithms

Detection of Fake Profiles and News on Social Media Using Ma...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Sachin Saxena Archana Singh Shailesh Tiwari Amity School of Engineering and Technology Amity University Noida India KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Technology is advancing rapidly every day, becoming an integral part of our lives and helping people connect with each other through social media and online news outlets. But as everything has its own advantages and disadvantages, it has too, and this paves a way for individuals to make fake news and accounts that are posing a major threat to society. This paper tries to incorporate a strategy utilizing machine learning algorithms. Random Forest, Support Vector machines and Neural Network are some of the algorithms which are used to distinguish fake profiles on social media and for detection of fake news online Naïve Bayes' and LSTM are utilized. Hence, detection of fake profiles and fake news online is possible using the above-mentioned frameworks. The outcome illustrates the ability to distinguish between fake and real profiles using extracted features by applying trained machine learning models. Naive Bayes, k-nearest neighbor, and Poisson calculation independently give precision of 87%, 88.3.5%, and 91.2% individually. The proposed approach shows that the algorithm neural networks system has an accuracy of 94.3%. The proposed work uses the machine learning approach for detection of tampered profiles on social media platforms.

关键词： Support vector machines machine learning algorithms Social networking (online) Neural networks Nearest neighbor methods Feature extraction Bayes methods Fake news Random forests Long short term memory

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Emboli Detection by machine learning algorithms using multi-level feature extraction

Emboli Detection by machine learning algorithms using multi-...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Ab Waheed Lone Ahmet Elbir Nizamettin Aydin Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

In real world, data understanding involves finding relationship between dependent and independents events, and usually this data is embedded in the form of images, audio, videos, speech, text and much more and to extract useful and representative information, feature extraction and representation is a front-end step. For machine learning algorithms, feature extraction is a preprocessing technique that helps in finding the relationship between different variables. In biomedical signal analysis such as cerebral emboli signal detection, feature extraction from samples constitutes an important step. In this paper, we performed two step feature extraction procedure. First we extracted Mel Frequency Cepstral Coefficients (MFCC) and biologically inspired Gamma-tone Cepstral Coefficients from Doppler signals. Second we extracted kurtosis and skewness parameters from all the Mel-frequency cepstral coefficients and Gamma-tone Cepstral Coefficients coefficients. To gain the understanding of extracted features from Doppler signals, we trained some machine learning algorithms such as k-nearest neighbors, support vector machines and logistic regression. GTCC based kurtosis and skewness features show better classification between emboli, artifact signals and Doppler speckle signals. We present evaluation results using confusion matrix for classification between emboli signals (ES), Doppler speckle (DS) and artifact signals (AS).

关键词： Wavelet transforms machine learning algorithms Scattering Pattern classification machine learning Speckle Feature extraction

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Multi-Label Classification of Legal Cases According to the Sustainable Development Goals Using machine learning algorithms

Multi-Label Classification of Legal Cases According to the S...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： José Alfredo Ferreira Costa Esdras Daniel de Sousa Araújo Silva Stefane de Oliveira Silva Nielsen Castelo Damasceno Dantas Dept. de Engenharia Elétrica Universidade Federal do Rio Grande do Norte Natal RN Brasil PEP –Programa de Pós-graduação em Engenharia de Produção Universidade Federal do Rio Grande do Norte Natal RN Brasil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Multi-label text classification plays a crucial role in various domains, enabling the automated categorization of documents into multiple categories simultaneously. This study explores the application of multi-label text classification techniques to a dataset of 30,000 legal documents in Brazilian Portuguese, specifically judicial decisions from the Court of Justice of Rio Grande do Norte. Our research focuses on leveraging problem transformation methods, including Binary Relevance and Classifier Chains, and comparing the performance of four different classifiers: Random Forest, XGBoost, Support Vector machine (SVM), and CatBoost. We utilize TFIDF to generate embeddings from the textual data and evaluate the classifiers' performance using metrics such as accuracy, Hamming Loss, and Zero-One Loss. Our findings indicate that XGBoost, particularly when combined with problem transformation methods, consistently outperforms other classifiers in terms of accuracy and robustness. This study provides valuable insights into the application of machine learning for classifying legal documents and contributes to the growing body of research on utilizing technology to support the achievement of the United Nations' Sustainable Development Goals (SDGs).

关键词： Support vector machines Measurement Accuracy machine learning algorithms Law Text categorization Multi label classification Robustness Sustainable development Random forests

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Image Analysis for Face Recognition and Detection Using machine learning algorithms

Image Analysis for Face Recognition and Detection Using Mach...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Admasu Yehule Teferi Hiyam Hatem Jabbar Qasim Abduljabbar Hamad Habitamu Endalamaew Kassahun Ayodeji Olalekan Salau Abdul Rahman Department of Electrical and Computer Engineering University of Gondar Gondar Ethiopia College of Computer Sciences and Information Technology University of Sumer Iraq Center of Computer University of Sumer Thi-Qar Iraq Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Dominant methods for security purposes have emerged as a public safety concern for modern society. In this study, we have have incorporated different algorithms like Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), and Voila-Jones (VJ) for the task of face recognition and detection. Each of these applications has its own distinct advantages and disadvantages. For example, PCA is often regarded as a simpler and more appropriate approach for differentiating faces within a given image. On the other hand, the Voila-Jones algorithm excels in the face detection mechanism, offering superior capabilities for detecting faces in two-dimensional settings. While these applications have demonstrated successful results, they may encounter challenges in accurately representing faces due to various factors. In this study, we have made a comparative investigation of the performance, accuracy, and effectiveness of these algorithms in their application in face recognition and detection tasks. This study was conducted and tested based on well-known face datasets, such as ORL and Yale data sets. We divided these datasets into training and testing sets to prepare the data. We then proceeded to perform face recognition and classification, considering the selection of input data. Finally, we computed the system’s accuracy. Moreover, this study focused on conducting face recognition and detection in 2-Dimenitional settings. For performance evaluation, we used various testing and training images. In the testing phase with 45 images, PCA showed an accuracy of 97.78%, whereas Voila-Jones algorithm achieved a detection accuracy of 100%. Furthermore, when testing with 40 images, LDA achieved an accuracy of 100%, however, KNN demonstrated an accuracy of 98.5%. However, the Viola-Jones algorithm achieved a slightly lower detection accuracy of 97.5%. Overall, our results showed that LDA outperformed the other methods and demonstrated better results.

关键词： Training Accuracy Image recognition machine learning algorithms Databases Face recognition Nearest neighbor methods Face detection Principal component analysis Testing

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Automated Tuna Freshness Assessment via Gas Sensors and machine learning algorithms

Automated Tuna Freshness Assessment via Gas Sensors and Mach...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Nyoman Raflly Pratama Ledya Novamizanti Dedy Rahman Wijaya School of Electrical Engineering Telkom University Bandung Indonesia School of Electrical Engineering Center of Excellence Artificial Intelligence for Learning and Optimization Telkom University Bandung Indonesia School of Appllied Science Telkom University Bandung Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Ensuring the safety and quality of fish products is essential for public health, especially in countries like Indonesia, where tuna is a popular fishery product. Tuna freshness is a critical indicator of seafood safety, directly influencing both its nutritional value and the risk of contamination. This study explores the application of machine learning models to accurately and efficiently classify tuna freshness based on gas emissions detected by an electronic nose (e-nose) system. The system employs MQ-135, MQ-9, and MQ-2 gas sensors, which are sensitive to spoilage-associated gases such as ammonia, methane, and alcohol. A dataset of 58,389 records was generated to capture the gas profiles released by tuna over time, creating a robust dataset for model training and validation. Three machine learning algorithms―K-Nearest Neighbors (KNN), Support Vector machine (SVM), and Naive Bayes―were compared to evaluate classification accuracy and practical applicability. Results indicate that both KNN and SVM achieved high accuracy rates of 99%, while Naive Bayes reached 90%, underscoring the suitability of KNN and SVM for tuna freshness detection. These findings demonstrate the potential of integrating e-nose technology and machine learning to provide a rapid, reliable, and scalable solution for freshness assessment in the fishing industry, enhancing quality control from suppliers to retailers.

关键词： Support vector machines Gases Accuracy machine learning algorithms machine learning Nearest neighbor methods Electronic noses Safety Classification algorithms Gas detectors

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An Ontology Alignment Validation Approach Based on Supervised machine learning algorithms and Automatic Schema Matching Approach

An Ontology Alignment Validation Approach Based on Supervise...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Faten Abbassi Yousra Bendaly Hlaoui Faculty of Sciences of Tunis LIPSIC Laboratory EI Manar Tunisia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The existence of various representations of the same ontology poses a challenge in terms of manipulating knowledge across different computational domains. To address this issue, it would be prudent to harmonise similar ontologies by reducing their level of heterogeneity. This proposed solution involves aligning comparable ontologies through the utilisation of established ontology schema-matching techniques. In this paper, we introduce an approach for ontology alignment that leverages these techniques along with machine learning algorithms. To accomplish this, we propose a method for constructing a matrix based on ontology matching techniques, specifically employing element matching and structure matching techniques facilitated by elementary matchers. Subsequently, once the matrix is constructed, we use a composite matcher as a classifier to determine the degree of similarity between the two ontologies. Since these matchers are not readily available, we put forth the idea of implementing them in this paper using various supervised machine learning algorithms such as Neural Network and Logistic Regression. To validate our approach, we conducted an empirical assessment using the Conference track and the benchmark track of the reference ontologies provided by the Ontology Alignment Evaluation Initiative (OAEI 1).

关键词： Logistic regression machine learning algorithms Neural networks Ontologies Benchmark testing Software

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