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检索条件"主题词=Machine Learning Algorithms"

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Crop Recommendation System using machine learning algorithms

Crop Recommendation System using Machine Learning Algorithms

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Gaurav Chauhan Alka Chaudhary Amity Institute of Information Technology Amity University Noida Uttar Pradesh

ISBN: (纸本)9781665439718

India is a predominantly agricultural country, with agriculture playing animportant part in the Indian economy and people’s lives. Crops are recommended based on soil, weather, humidity, rainfall, and other variables to increase agricultural output. It benefits not just farmers, but also the country and helps to keep food costs *** paper presents the utilisation of machine learning approaches like Random Forest and Decision Tree to predict which crop is best for which soil type based on the data sets.

关键词： machine learning algorithms Costs Crops Humidity Soil Market research Agriculture

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Short-term Wind Speed Forecasting using machine learning algorithms

Short-term Wind Speed Forecasting using Machine Learning Alg...

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Power Tech Conference

作者： Sebastiao B. Fonseca Roberto Celio L. de Oliveira Carolina M. Affonso Faculty of Electrical and Biomedical Engineering Federal University of Para Belem Brazil

This paper evaluates the performance of several machine learning algorithms for short-term wind speed forecasting. The algorithms evaluated include: Long Short-Term Memory, Extra-Tree, Gradient Boosting Tree, Extreme Gradient Boosting Tree, Voting Averaged, Multi-layer Perceptron, K-Nearest Neighbors, and Support Vector machine. The performance of the algorithms was evaluated with different error metrics using real wind speed and meteorological data collected from the city of Maceio, Brazil. First, pre-processing methods are applied in the large database to deal with outliers, noisy and missing values. Then, variable selection technique is employed to select the most significant set of variables and their lag-values as input to the forecast algorithm. Results show Voting Averaged algorithm performs better for all forecast time horizons considered, which are 1 hour, 2 hours and 3 hours ahead.

关键词： Support vector machines machine learning algorithms Databases Wind speed Input variables Urban areas Boosting

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Heart Disease Prediction Using Supervised machine learning algorithms

Heart Disease Prediction Using Supervised Machine Learning A...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Narendra Mohan Vinod Jain Gauranshi Agrawal GLA University Mathura India

ISBN: (纸本)9781665447874

Predicting and detecting cardiac disease has always been a difficult and time-consuming undertaking for doctors. To treat cardiac disorders, hospitals and other clinics are giving costly therapies and operations. As a result, anticipating cardiac disease in its early stages will be beneficial to people all around the world, allowing them to take required treatment before it becomes serious. Heart disease has been a major issue in recent years, with the primary causes being excessive alcohol use, tobacco use, and a lack of physical activity. machine learning methods are utilized to forecast cardiac illnesses in this article. For training and testing, a data collection containing diverse human health parameters is used. Many AI&ML algorithms are used to predict cardiac disorders. The performance of the machine learning algorithm is compared after it has been implemented.

关键词： Heart Training machine learning algorithms Hospitals Cardiac disease machine learning Prediction algorithms

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Analysis of machine learning algorithms in Big Data Analytics

Analysis of Machine Learning Algorithms in Big Data Analytic...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Er. Sandeep Kaur Anand Kumar Shukla Chandigarh University Punjab India

ISBN: (纸本)9781665439718

Massive information being a brilliant resource of information and understanding from structures to the different end users. However, managing this much quantity of information wishes automation, thus leading to a fashion of statistics processing along with gadget mastering techniques. Inside the ict region, as in various areas of trade and evaluation, structures and equipment have been furnished and advanced in assisting the specialists to deal with the information and study from it routinely. Maximum of the systems returned from large corporations such as Microsoft or Google, or from the apache foundation’s incubators. This evaluation reveals gadget mastering algorithms in big records analytics, and gadget mastering challenges us to make selections where it may be no recognized "right course" for the specified trouble based on the previous training and tallies a number of the headmost used gear to analyze and model massive-statistics.

关键词： Training Analytical models machine learning algorithms Automation Gears Collaboration Big Data

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A Comparative Study to Classify and Predict the Throughput of Fifth Generation Wireless Technology Using Supervised machine learning algorithms

A Comparative Study to Classify and Predict the Throughput o...

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International Conference on Image Information Processing

作者： Abhilasha Sharma Shweta Pandit Salman Raju Talluri Eternal University India Jaypee University of Information Technology India

ISBN: (纸本)9781665433624

In the modern era, the demand for 5 th generation (5G) communication technology is increasing day by day due to the increased data rate, higher bandwidth, and lower delay time of 5G. To find the throughput range or its expected value in a particular slot, the classification and regression models are used. The present research applies three machine learning algorithms to predict and classify the throughput of 5G. The data for this study is obtained from the internet repository. Two classification models and two regression models are tested to predict the throughput of the millimeter wave (mm-wave) 5G dataset. The performance of classification algorithms is verified using precision, recall, F1 score, overall classification accuracy, and speed. It is observed that the random forest (RF) classifier achieves better values of all the performance parameters as compared to the support vector machine (SVM) classifier. The performance of the regression models is checked using root mean square error, correlation, R-square, and execution time. The experimental results show that the random forest model achieves better values of these parameters as compared to the generalized linear regression model (GLM). In addition, the observations show less execution time of the generalized linear model than the random forest model.

关键词： Wireless communication machine learning algorithms Correlation 5G mobile communication Linear regression Support vector machine classification Predictive models

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Performance Analysis of machine learning algorithms in Generating Urban Land Cover Map of Quezon City, Philippines Using Sentinel-2 Satellite Imagery

Performance Analysis of Machine Learning Algorithms in Gener...

引用

International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Robert Martin Santiago Reggie Gustilo Gerald Arada Elmer Magsino Edwin Sybingco De La Salle University Manila Philippines

ISBN: (纸本)9781665401685

As urban expansion is expected to persist and may even accelerate in the coming years, understanding and effectively managing urbanization become increasingly important in achieving long-term progress specifically in making cities and human settlements inclusive, safe, resilient, and sustainable. One way to accomplish these is to obtain reliable and updated information about the land cover characteristics of an area in the form of a map which can be done using remote sensing and machine learning. However, the practice of using these technologies for urban land cover mapping was observed to occur in the geographic locality level, and in the case of the Philippines, this is a domain that needs to be further explored to quantitatively comprehend urban extent. In this study, a map of man-made structures or built-up areas and natural structures or nonbuilt-up areas was generated over Quezon City and nearby surrounding areas where rapid rise in population occurs along with urban development. In addition, since related previous studies used various machine learning algorithms in doing the classification, this study compared the performances of three algorithms specifically random forest classifier, k-nearest neighbors, and Gaussian mixture model to identify which performed best in this particular application. The satellite imagery of the area of interest was collected from the Sentinel-2 mission satellites. All the three algorithms attained high accuracies across all measurements with small variations but greatly differed in the time consumed doing the classification. The highest over-all accuracy of 99.32% was obtained using random forest classifier despite taking the longest time to finish the classification, next is 98.95% using the k-nearest neighbors algorithm which also ranked second in terms of speed of classification, and last is 97.17% using the Gaussian mixture model despite being the fastest to complete the classification. Further studies may explore other machi

关键词： machine learning algorithms Satellites Urban areas Sociology Time measurement Classification algorithms Spatial resolution

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Computational Analysis of machine learning algorithms to Predict Heart Disease

Computational Analysis of Machine Learning Algorithms to Pre...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Kunal Rao Pawan Raj Gopal Kusum Lata University School of Management and Entrepreneurship Delhi Technological University Delhi India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Heart disease is one of the most common problems and also a disease whose rate of increase has been higher in recent years. The complex task associated is exploitation of hidden patterns for effective and accurate prediction. There is plenty of data generated every year from various health institutes. This research focuses on development of supervised machine learning models for prediction of target heart disease. We have used publicly available heart disease datasets from University of California, Irvine (UCI) data repository for Cleveland, Switzerland, Hungarian and Long Beach. Various forms of preprocessing steps such as handling the missing values and null values, removal of duplicate entries is employed on these datasets in order to use it for developing effective models. The correlation between the features set and the target variable is studied. The prediction models are developed using effective machine learning techniques like Logistic Regression, Decision Trees, Naive Bayes, K-nearest neighbors, ensembles AdaBoost and XGBoost. The predictive performance of the developed models is compared with the help of stable accuracy measures like accuracy, precision, recall, F1-seore, Cohen's kappa and Area Under the Curve score. K- nearest neighbors was the best model for the Cleveland dataset with 86.81% accuracy. AdaBoost algorithm gave us the highest accuracy of 98% for Switzerland dataset. Bernoulli Naive Bayes predicted the heart diseases for Hungarian dataset with 84.26% accuracy. XGBoost gave better accuracy of 82.20% for VA Long Beach dataset. The results of the study advocate the applicability of machine learning techniques to predict heart disease.

关键词： Heart machine learning algorithms machine learning Predictive models Task analysis Regression tree analysis Diseases

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Classifying Denial of Service Attacks Using Fast machine learning algorithms

Classifying Denial of Service Attacks Using Fast Machine Lea...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhida Li Ana Laura Gonzalez Rios Ljiljana Trajković School of Engineering Science Simon Fraser University Vancouver Canada

ISBN: (纸本)9781665442084

Denial of service attacks are harmful cyberattacks that diminish Internet resources and services. Hence, detecting these cyberattacks is a topic of great interest in cybersecurity. Using traditional machine learning approaches in intrusion detection systems requires long training time and has high computational complexity. Thus, we evaluate performance of fast machine learning algorithms for training and generating models to detect denial of service attacks in communication networks. We use synthetically generated datasets that captured Transmission Control Protocol and User Datagram Protocol network flows in a controlled testbed laboratory environment. Evaluated algorithms include broad learning system and its extensions as well as XGBoost, LightGBM, and CatBoost gra-dient boosting decision tree algorithms. Experiments indicate that boosting algorithms often require shorter training time and have better performance.

关键词： Training learning systems machine learning algorithms Sensitivity Protocols Laboratories Boosting

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Evaluation and Comparison of machine learning algorithms for Solar Flare Class Prediction

Evaluation and Comparison of Machine Learning Algorithms for...

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Artificial Intelligence and machine Vision (AIMV), International Conference on

作者： Savita R. Gandhi Aishawariya Athawale Hetvi Julasana Suchit Purohit Gujarat University Ahmedabad India

ISBN: (纸本)9781665442121

Due to powerful and sudden release of magnetic energy, solar flares pose a great threat to technological systems in space as well as on ground. This study explores the potential of machine learning in predicting the class of solar flare namely- B: weakest flare, C: weak flare, M: strong flare, X: strongest flare and N: no flare. The study aims to apply machine learning algorithms on SDO/HMI vector magnetic field data obtained by the Space-weather HMI Active Region Patches (SHARP) and assess the performance of different machine learning algorithms namely Logistic Regression, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Decision tree, Random Forest, Adaptive Boosting and Gradient Boosting with respect to different performance metrics. Of all applied algorithms, Random Forest was found to outperform other classification algorithms.

关键词： Support vector machines Measurement machine learning algorithms Space technology Prediction algorithms Boosting Classification algorithms

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Enhanced Ransomware Detection Techniques using machine learning algorithms

Enhanced Ransomware Detection Techniques using Machine Learn...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： G. Usha P. Madhavan Meenalosini Vimal Cruz N A S Vinoth Veena Maria Nancy SRM IST KTR SRM KTR Allen E. Paulson Georgia Southern University

ISBN: (纸本)9781665414487

A challenge that governments, enterprises as well as individuals are constantly facing is the growing threat of ransomware attacks. Ransomware is a type of malware that encrypts the user's files and then demands a huge sum of money from the user. This increasing complexity calls for more advancement and innovative ideas in defensive strategies used to tackle the problems. In this paper, firstly we discuss the existing research in the field of ransomware detection techniques and their shortcomings. Secondly, a juxtaposed study on various machine learning algorithms to detect ransomware attacks is compared for ransomware dataset. Thirdly, various behavioral data such as API Calls, Target files, Registry Operations, Signature, Network Accesses are collected for each ransomware and benign sample and the results are compared for various attributes to understand the behavior of the attack. In order to understand the behavior of the attack various machine learning algorithms like KNN, Naïve Bayes, Random Forest, Decision Trees are used for training and testing the dataset.. Further optimization was done using hyper parameters to control the learning process. Finally, we have used the model(s) Accuracy, F1 Score, Precision and Recall to compare the results observed and suggesting how the roadmap for how efficiently the attacks can be prevented in future.

关键词： Training machine learning algorithms Government Process control Forestry Ransomware Decision trees

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