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检索条件"主题词=Machine Learning Algorithms"

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Implementation of machine learning algorithms to Detect the Prognosis Rate of Kidney Disease

Implementation of Machine Learning Algorithms to Detect the ...

引用

Innovation in Technology (INOCON), IEEE International Conference for

作者： F. M. Javed Mehedi Shamrat Pronab Ghosh Mahbubul Hasan Sadek Md. Aslam Kazi Shahana Shultana Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728197449

ISBN: (纸本)9781728197456

The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a significant amount of lives are lost annually due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.

关键词： Diseases Prediction algorithms Classification algorithms machine learning algorithms Kidney Random forests Logistics

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Forecasting Meteorological Analysis using machine learning algorithms

Forecasting Meteorological Analysis using Machine Learning A...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Bhagya Lakshmi Pavuluri Ramya Sree Vejendla Pavuluri Jithendra Tinnavalli Deepika Shahana Bano Department Of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9781728154619

ISBN: (纸本)9781728154626

Weather prediction is gaining up ubiquity quickly in the current period of machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integral asset which has been utilized in several prediction works for instance, flood prediction, storm detection etc. In this paper, a simple approach for weather prediction of future years by utilizing the past data analysis is proposed by the decision tree, K-NN and random forest algorithm calculations and showing the best accuracy result of these three algorithms. Weather prediction plays a significant job in everyday applications and in this paper the prediction is done based on the temperature changes of the certain area. All these algorithms calculate the mean values, median, confidence values, probability and show the difference between plots of all the three algorithms etc. Finally, using these algorithms in this work we can predict whether the temperature increases or decreases, is it a rainy day or not. The dataset is completely based on the weather of certain area including few objects like year, month, and temperature, predicted values and so on.

关键词： Prediction algorithms Decision trees Classification algorithms Weather forecasting machine learning algorithms Forestry

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Into the Unknown: Unsupervised machine learning algorithms for Anomaly-Based Intrusion Detection

Into the Unknown: Unsupervised Machine Learning Algorithms f...

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IEEE/IFIP International Conference on Dependable Systems and Networks â€“ Supplemental Volume (DSN-S)

作者： Tommaso Zoppi Andrea Ceccarelli Andrea Bondavalli Department of Mathematics and Informatics University of Florence Florence (IT)

ISBN: (数字)9781728172606

ISBN: (纸本)9781728172613

Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior, relying on machine-learning algorithms that are suited for binary classification. It has been arising as one of the most promising techniques to suspect intrusions, zero-day attacks and, under certain conditions, failures. This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able to classify normal and anomalous behaviors without relying on input data with labeled attacks.

关键词： Anomaly detection Intrusion detection Tutorials Tools Software algorithms machine learning algorithms machine learning

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Diversity Factor Prediction for Distribution Feeders with Interpretable machine learning algorithms

Diversity Factor Prediction for Distribution Feeders with In...

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IEEE General Meeting Power& Energy Society

作者： Wenyu Wang Nanpeng Yu Jie Shi Nery Navarro University of California Riverside Riverside USA Grid Modernization Southern California Edison Pomona USA

ISBN: (数字)9781728155081

ISBN: (纸本)9781728155098

The maximum diversified demand is an important factor to consider when utilities design new distribution systems. To estimate the maximum diversified demand, engineers need to make an estimate of the diversity factor (DF). In practice, electricity utility companies usually estimate the DF using DF tables, in which the DF changes with the number of customers. However, besides the number of customers, DF also depends on many other factors, such as customer type, weather, demographics, and socioeconomic conditions. Ignoring these factors, DF tables have limited accuracy. In addition, engineers cannot interpret or understand how various factors affect the DF. In this paper, by leveraging supervised machine learning algorithms, we build comprehensive DF prediction models that take a variety of factors into account. These models show high prediction accuracy and interpretabilty when applied to real-world distribution feeders. Using the interpretation method called SHapley Additive exPlanations, we quantity the importance of different features in determining DFs. Finally, we offer more insights into how various factors affect DFs.

关键词： machine learning algorithms Additives machine learning Companies Predictive models Cultural differences Meteorology

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A Comprehensive Study on machine learning algorithms for Wireless Sensor Network Security

A Comprehensive Study on Machine Learning Algorithms for Wir...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Fozilatoon Humaira Md Sanju Islam Fernaz Narin Nur Kazi Anowar Hussain Notre Dame University Bangladesh Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Wireless sensor network is one of the most hopeful technologies for its small-shape, low-price, and simply circulated behavior. It may be altered dynamically because of some exterior or interior causes. The consecutive methods have been bluntly programmed that anticipate the networks difficult to react dynamically. For overcoming this kind of situation, machine learning approaches can be exercised to respond correctly. machine learning is the procedure of learning from the expertness and actions without human help or re-program. There are many opportunities present in this field and we have highlighted some of them in this survey.

关键词： Wireless sensor networks machine learning algorithms Security machine learning Training Communication system security Data models

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Application for Traffic Classification Using machine learning algorithms

Application for Traffic Classification Using Machine Learnin...

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IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Evgenii Nazarenko Vitalii Varkentin Aleksey Minbaleev Chair of Comprehensive Information Security South Ural State University (national research university) Chelyabinsk Russia Kutafin Moscow State Law University (MSAL) Moscow Russia

ISBN: (数字)9781728181790

ISBN: (纸本)9781728181806

The timely detection of malicious traffic and the prevention of DDoS attacks is an important task in today's informational environment. There was considered a theoretical part of the classical machine learning algorithms (the principles of their work and the information about parameters). The Functional and non-functional requirements were identified then the action schemes and the use cases were presented. There was developed a traffic classification system appearing as a window application. There was presented the implementation of the classical machine learning algorithms and a user's interface. The developed network traffic identification and classification system uses the machine learning algorithms: k-NN, Naive Bayes, SVM, Ridge / Lasso, Decision Tree and k-Means. The accuracy of the system ranged from 46.69% to 99.81% depending on which algorithm was used.

关键词： machine learning algorithms Classification algorithms Telecommunication traffic Denial-of-service attack Task analysis Training Support vector machines

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Identification of Most Relevant Breast Cancer miRNA using machine learning algorithms

Identification of Most Relevant Breast Cancer miRNA using Ma...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Tasnime Mehejabin Faria Rahman Soniya Yeasmin Manika Sarkar North Western University Khulna Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Day by day, biological data is increasing and researchers face various problems to extract informative data from a large scale of dataset. Cancer has been identified as most common and unexpected disease worldwide. In our proposed model, we build a combined machine learning model using some efficient feature selection algorithm and then analyses it through ANN and high performance classification algorithms. We also implement ensemble methods for building our model more accurate. For verifying our model we implement breast cancer miRNA data. Chi Squared Test gives the minimum relevant features where we get 99.23% accuracy from SVM. We use 10 fold cross validation to test the dataset. The result is promising and encouraging. With the processing of the data, our system model able to identify most relevant features responsible for breast cancer.

关键词： Feature extraction Classification algorithms Support vector machines Breast cancer machine learning algorithms machine learning Biological system modeling

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Cardiovascular Disease (CVD) Prediction using machine learning algorithms

Cardiovascular Disease (CVD) Prediction using Machine Learni...

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IEEE India Conference (INDICON)

作者： Ibashisha A. Marbaniang Nurul Amin Choudhury Soumen Moulik National Institute of Technology Meghalaya Shillong India

ISBN: (数字)9781728169163

ISBN: (纸本)9781728169170

This paper deals with the prediction of Cardiovascular Disease (CVD) by performing an analysis of six supervised machine learning algorithms such as K-Nearest Neighbors Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier, Support Vector machine Classifier and Linear Discriminant Analysis. Further, by introducing two health risk factors (feature extraction) - Blood Pressure and Body Mass Index - into the dataset, we have observed an increase in the accuracy. Feature selection was performed to find out the important risk factors. Our main goal is to find the most optimal results in terms of CVD prediction from the available dataset.

关键词： Support vector machines machine learning algorithms Feature extraction Blood pressure Cardiovascular diseases Indexes Random forests

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Multi-level Predictive with Training Framework (MP with TF) for ranking machine learning algorithms

Multi-level Predictive with Training Framework (MP with TF) ...

引用

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

作者： Terrance Frederick Fernandez M. Pradeep Rajiv Gandhi College of Engineering and Technology Puducherry INDIA

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154657

machine learning algorithms differ from conventional ones in a way that they adjust themselves to perform better when exposed to surplus data. Huge volumes of data were pipelined from the grades scored by student over the last `n' years. Among the data earned from these, `k' years were populated as trained set and `(n-k)' as test sets. For the proposed Multi-level Predictive with Training Framework (MP with TF), five AI based approaches such as k-nearest neighbors, neural networks, Logistic regression, Naive Bayes and Random forest were considered to predict the target variable set. The performance of these machine learning algorithms were implemented using the actual grades scored by the students in their End Examination through a data science toolkit. Furthermore, the performances of these algorithms were ranked based on the accuracy of predicting the grades scored by the students.

关键词： Training machine learning algorithms Neural networks Forestry Prediction algorithms Random forests Logistics

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Efficiency of Different machine learning algorithms on the Multivariate Classification of IoT Botnet Attacks

Efficiency of Different Machine Learning Algorithms on the M...

引用

IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Shreehar Joshi Eman Abdelfattah School of Theoretical & Applied Science Ramapo College of New Jersey Mahwah NJ

ISBN: (数字)9781728196565

ISBN: (纸本)9781728196572

The Internet of Things, with its enormous growth in the recent decades, has not just brought convenience to the different aspects of our lives. It has also increased the risks of various forms of cybercriminal attacks, ranging from personal information theft to the disruption of the entire network of a service provider. As the demands of such devices increase rapidly on a global scale, it has become increasingly difficult for different corporations to focus on security efficiently. As such, the demand for methodologies that can aptly respond to prevent intrusion within a network has soared disturbingly. Various utilization of anomaly traffic detection techniques has been conducted in the past, all with the similar aim to prevent disruption in networks. This research aims to find an efficient classifier that detects anomaly traffic from N_BaIoT dataset with the highest overall precision and recall by experimenting with four machine learning techniques. Four binary classifiers: Decision Trees, Extra Trees Classifiers, Random Forests, and Support Vector machines are tested and validated to produce the result. The outcome demonstrates that all the classifiers perform exceptionally well when used to train and test the anomaly within a single device. Moreover, Random Forests classifier outperforms all others when training is done on a particular device to test the anomaly on completely unrelated devices.

关键词： Classification algorithms Prediction algorithms Vegetation machine learning algorithms Botnet Support vector machines Security

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