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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是581-590 订阅

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Classification of Pulsar Candidates Using Classical machine learning algorithms

Classification of Pulsar Candidates Using Classical Machine ...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Siddharth Kannan Aakash Dhananjay Shanbhag 12th Grade A-Levels Prabhavati Padamshi Soni Intl. Junior College Juhu Mumbai India

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

Pulsars, rapidly spinning neutron stars with powerful radio emissions, are of significant interest in astrophysics. This study examines the effectiveness of various machine learning methods-Random Forest, XGBoost, Logistic Regression, Support Vector machines (SVM), and Gradient Boosting-in identifying pulsars from a given dataset. We addressed the challenge of data imbalance, where pulsars were under-represented, resulting in an accuracy increase from 82.4% to 93.7% after correction. Additionally, we removed higher-order central moments from the data and found that this had no statistically significant impact on prediction accuracy. Our analysis of feature importance revealed that attributes from the Integrated Profile are more crucial for identifying pulsars than those from the DM-SNR curve.

关键词： Support vector machines Logistic regression Accuracy Neutron stars Mechatronics machine learning algorithms machine learning Forestry Predictive models Spinning

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Leukemia Subtype Classification via Gene Expression Using machine learning algorithms

Leukemia Subtype Classification via Gene Expression Using Ma...

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

作者： Roshani Raut Shreya Lokare Shraddha Gavkare Mayuri Narute Dept. of Information Technology Pimpri Chinchwad College of Eng. Pune India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This project aims to improve patient care and treatment outcomes by using gene expression data to reliably diagnose acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). To create trustworthy classification models, six machine learning (ML) techniques were used: Gaussian Naive Bayes (NB), Random Forest, Support Vector machine (SVM), K Nearest Neighbors (KNN), Multi-Layer Perceptron(MLP), and Logistic Regression. The experiment's outcomes show that the algorithms were successful; 91% accuracy rates were achieved by both logistic regression and MLP, while the other approaches produced results that were competitive. Good findings for the ROC curve analysis area under the curve (ROCAUC) demonstrated the models' ability to distinguish between different forms of cancer. These results demonstrate the application of ML algorithms in oncology.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Soft sensors Vectors Classification algorithms

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Chronic Kidney Disease Prediction Using machine learning algorithms

Chronic Kidney Disease Prediction Using Machine Learning Alg...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： K. Santhiya D. Murugan K. Navaz Department of CSE Manonmaniam Sundaranar University Tirunelveli Tamil Nadu India Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Chronic Kidney Disease (CKD) stands as a pervasive and critical renal condition, posing a substantial global health challenge. Each kidney comprises nephrons, the essential filtration units that help to filter the blood and extract the waste products. Individuals with type 2 diabetes or high blood pressure face an elevated risk of renal failure. Predicting renal failure at an early stage remains challenging due to the absence of specific symptoms, leading to severe long-term consequences, including mortality. This paper aims to comprehensively analyze and assess the efficacy of predictive models for CKD, using various machine learning Classification algorithms. The classifiers such as K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and Gradient Boosting (GB). Out of all the classifiers Random Forest and Gradient Boosting produced the best accuracy result compared to other classifiers. This research extends the investigation by providing a nuanced analysis of the significance of machine learning techniques in predicting CKD, emphasizing their role in early detection and prognosis. This research includes a detailed comparison of accuracy scores and, a confusion matrix to identify correct and incorrect features, thereby facilitating a comprehensive examination and analysis of machine learning algorithms. The evaluation utilizes accuracy, precision, recall, and f1-score to detect the optimal model.

关键词： machine learning algorithms Accuracy Nearest neighbor methods Boosting Prediction algorithms Chronic kidney disease Classification algorithms Bayes methods Decision trees Random forests

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Proactive algorithms for Stabilizing the State of Technological Processes Based on Metric machine learning algorithms

Proactive Algorithms for Stabilizing the State of Technologi...

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International Russian Automation Conference (RusAutoCon)

作者： A. Musaev A. Makshanov D. Grigoriev St. Petersburg State Institute of Technology (Technical University) Saint-Petersburg Russia Department of Computing Systems and Computer Science Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia Center for econometrics and business analytics (CEBA) St. Petersburg State University (SPBU) Saint-Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.

关键词： Measurement machine learning algorithms Statistical analysis Heuristic algorithms Process control Training data Predictive models Random processes Forecasting Monitoring

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Optimization of Human Resource Recruitment Efficiency Based on machine learning algorithms

Optimization of Human Resource Recruitment Efficiency Based ...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Ting Yu Beijing Xindazheng Aviation Technology Service Co. Ltd Beijing China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the rapid development of information technology, the application of information management systems has penetrated into various aspects of enterprise operation, becoming a key factor in promoting continuous innovation and development of enterprises. In this context, human resource management has also ushered in a wave of digital transformation, among which online recruitment, as an important way to acquire talent, has received widespread attention for its efficiency and accuracy. Although online recruitment provides job seekers and recruiting units with a broader platform and convenient channels, in practical operation, it still faces many challenges such as low efficiency in personnel position matching and imbalanced matching results. How to accurately and quickly complete job matching has become an urgent problem in the field of human resources. In response to this situation, this article proposes a human resources recruitment system based on machine learning (ML) algorithm. This system integrates advanced ML technology to deeply analyze and mine massive recruitment and job search data, in order to achieve precise matching between job seekers and recruitment positions. The experimental results show that the system has achieved significant results in improving the efficiency of personnel position matching.

关键词： Knowledge engineering Technological innovation machine learning algorithms Accuracy machine learning Information management Personnel Information technology Optimization Recruitment

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A Novel Study of Predicting Coronary Artery Disease with Data Mining and machine learning algorithms

A Novel Study of Predicting Coronary Artery Disease with Dat...

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Healthcare Innovations, Software and Engineering Technologies (HISET), International Conference on

作者： Vibhor Sharma Deepak Srivastava Vinay Avasthi Department of Computer Science & Engineering Swami Rama Himalayan University Dehradun India

ISBN: (数字)9798350360707

ISBN: (纸本)9798350360714

The prevalence of health problems is rising. Coronary Artery disorders, in particular, are commonplace now. Today, Coronary Artery Disease is the leading cause of death worldwide, especially in poorer nations. Reducing mortality from Coronary Artery infections requires both early detection and ongoing clinical care. However, due to the increased effort and expertise required, thorough and uniform patient checks are just not feasible. The healthcare fields store an enormous amount of data. However, most of it is wasted since it isn't being used effectively. Data storage has the potential to yield positive insights into the realm of future health prediction. The use of data sets and efficient tools to extract information for the scientific investigation of disease or other purposes is still in its infancy. Various information extraction and machine learning methods may be used to sift through the various patient heart variables and extract relevant data. The purpose of this article is to incorporate the latest research on relative outcomes of coronary illness anticipation and to build scientific goals by employing mining of data and classification approaches based on machine learning.

关键词： Heart machine learning algorithms Software algorithms machine learning Medical services Prediction algorithms Classification algorithms Data mining Arteries Diseases

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A Comparative Analysis on a Diabetic Disease Prediction Considering Various machine learning algorithms

A Comparative Analysis on a Diabetic Disease Prediction Cons...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Dilip Kumar Das Suman Chowdhury Mohammed Motaher Hossain Department of Mathematics International University of Business Agriculture and Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Quantitative Science International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetes is a serious health concern almost all over the countries as it impaired many organs in human body and rate of affected person is gradually increasing. Several significant factors have been incorporated for prediction of diabetic diseases through the machine learning attribute. In this study a non-communicable disease, diabetes, is predicted based on several machine learning approaches like Support vector machine (SVM), Decision tree (DT), Logistic regression (LR), K-Nearest Neighbours (KNN), Gaussian Naive Byes(GNB),Random Forest(RF). Data set of Sylhet Diabetic Hospital, Sylhet in Bangladesh containing 520 samples is collected from online source. Comparing the classifiers SVM shows the maximum accuracy 99%. Since a diabetic patient suffers immensely throughout the life, diagnosis along with proper medication is important.

关键词： Support vector machines Radio frequency Logistic regression Accuracy machine learning algorithms Nearest neighbor methods Diabetes Random forests Medical diagnostic imaging Diseases

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Early Diabetes Prediction using machine learning algorithms

Early Diabetes Prediction using Machine Learning Algorithms

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Riya Sinha Bandi Sai Vennela S. Babu Department of Computing Technologies SRM Institute of Science & Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

A chronic metabolic illness called “Diabetes mellitus”, with its rising prevalence and related consequences, poses a serious threat to global health. Early diabetes detection is essential for prompt intervention and efficient care, which lowers the risk of complications. machine learning approaches have recently become more popular in the healthcare industry due to their capacity to analyze big datasets and uncover insightful patterns. This work suggests a thorough strategy for predicting early diabetes using machine learning *** construction and assessment of predictive models based on various clinical and lifestyle parameters is the main goal of the study. An individual’s dietary preferences, medical history, and patterns of physical activity are all included in the dataset. The most pertinent variables are found using feature selection approaches, which improves the prediction models’ precision and effectiveness. The importance of several machine learning methods, such as decision trees, neural networks, SVM in predicting diabetes is tested.

关键词： Support vector machines machine learning algorithms Accuracy Neural networks Medical services Predictive models Prediction algorithms

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Prediction of Breast Cancer Using Simple machine learning algorithms

Prediction of Breast Cancer Using Simple Machine Learning Al...

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

作者： Seeta Devi Dipali Dumbre Ranjana Chavan Symbiosis College of Nursing (SCON) Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Breast cancer is a significant cause of morbidity and mortality among women, especially in developing countries. Early prediction, and accurate treatment are critical to reduce death rates. Using artificial intelligence and machine learning techniques, this project will evaluate the performance of several models in predicting breast cancer. The prediction models such as Decision Tree, Gradient Boosting, Naïve Bayes, Neural Network, SGD, kNN, and CN 2 rule inducer are employed utilizing Orange tools. This study used publicly accessible secondary data from Keggle, enabling transparency and accessibility in the evaluation approach. The findings disply that the DT, GB, Neural Network, and CN2 rule inducer had higher accuracy ratings of $0.992,0.998,0.997$, and 0.997, respectively, with exceptional AUC values of $0.989,1.000,1.000$, and 0.990. Additionally, their recall and accuracy scores are 0.992, $0.987,0.998$, and 0.997, demonstrating their strong performance in breast cancer prediction. Among the most popular classifier models, GB and Neural Network, are the outperformed models than others.

关键词： Accuracy machine learning algorithms Computational modeling Neural networks Predictive models Developing countries Prediction algorithms

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Comparative Analysis of machine learning algorithms and Datasets for Detecting Cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： Anusha Palagati Santhosh Kumar Balan S Arun Joe Babulo Laxmi Raja K.K. Natarajan Rajkumar Kalimuthu Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering SMIEEE Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tiruchengode Tamilnadu India Department of Cyber Security Karpagam Academy of Higher Education Coimbatore Tamilnadu India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.

关键词： Support vector machines Computers Logistic regression machine learning algorithms Supervised learning Optical character recognition Cyberbullying Bayes methods Object recognition Random forests

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