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检索条件"主题词=Machine Learning Algorithms"

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Optimized Sentiment Analysis of Hotel Reviews using machine learning algorithms

Optimized Sentiment Analysis of Hotel Reviews using Machine ...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： D Navanith Kona Likhith Mandaloju Sai Vardhan S. Kavitha Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (纸本)9781665482721

On social media, words and phrases reflect people's opinions about certain goods, services, organisations, laws, and events. In the discipline of NLP, the objective of sentiment analysis is to extract positive things or negative things from social media. Researchers are driven to complete their sentiment analysis research because of the government and corporate organisations' and individuals' exponentially growing needs. This study optimises sentimental analysis using four cutting-edge machine learning classifiers: Naivebayes, J48, BFTree, and OneR. This four classification methods' effectiveness are investigated and contrasted. While OneR appears more promise in producing the accuracy of correctly classified instances, Naivebayes was proven to be quick at learning. This study has observed that the four records are positively related, and the product's position in online market has also advanced over time. In light of the analysis of the favourable reviews and star ratings, this research study has suggested displaying a potentially successful or disastrous item based on the positive surveys. Finally, the relationship between the quantity of surveys and star ratings are also analyzed.

关键词： Sentiment analysis machine learning algorithms Social networking (online) Convolution Government Stars machine learning

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Landslide Likelihood Prediction using machine learning algorithms

Landslide Likelihood Prediction using Machine Learning Algor...

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IEEE International Conference on Big Data

作者： Vasundhara Acharya Anindita Ghosh Inwon Kang Thilanka Munasinghe K C Binita Rensselaer Polytechnic Institute Troy USA NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) /Adnet Systems Inc NASA Goddard Space Flight Center Greenbelt MD USA

ISBN: (纸本)9781665480468

The supply of electricity via power plants is critical to the operation of many critical infrastructure systems in modern society. Natural hazards can disrupt the power supply, cause power outages that can halt economic growth, and impede emergency response until power is restored. The proposed work aims to predict the landslides likelihood in these critical infrastructure locations in the Northeastern USA using integrated databases of explanatory variables and machine learning algorithms. First, data related to landslides are obtained and merged, including topographic, soil moisture, and precipitation-related data. Five regression algorithms, namely: Random Forest, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor regression (KNN), Linear Support Vector Regressor (SVR), and Linear regression, are utilized to predict the landslide probability and evaluated on the dataset. The accuracy of the models is assessed by using statistical metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The study results show that Random Forest outperformed other models with the mutual information feature selection method. It achieved an MSE of 0.0011 with mutual information-based feature selection and an MSE of 0.00157 without feature selection. KNN regressor outperformed the other models with an MSE of 0.00139 with correlation-based information selection. The proposed landslide identification model with Random Forest algorithm shows outstanding robustness and great potential in tackling the landslide likelihood prediction by employing ML algorithms.

关键词： Support vector machines Landslides machine learning algorithms Biological system modeling Soil moisture Big Data Prediction algorithms

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Forecasting Solar Photovoltaic Power Output in the German Jordanian University in Amman Using Artificial Intelligence and machine learning algorithms

Forecasting Solar Photovoltaic Power Output in the German Jo...

引用

International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Sahar Qaadan Aiman Alshare Mechatronics Engineering Department German Jordanian University Madaba Jordan Mechanical and Maintenance Engineering German Jordanian University Madaba Jordan

ISBN: (纸本)9781665490498

Forecasting solar photovoltaic power output is a difficult task, but it is essential for effective solar energy planning. Artificial intelligence and machine learning algorithms are promising to improve the forecasting accuracy. In this work, we discuss how different types of data (e.g. weather, solar irradiance, etc.) can be used to improve the accuracy of the forecast. We evaluate the effect of using machine learning algorithms, including artificial neural networks, support vector machines, and gradient boost to design the model that effectively represent the output power of a photovoltaic solar system in Jordan. Experimental results show that algorithms such as gradient boost perform the best compared to simulation and other machine learning algorithms. Best model selection applies 10-fold cross validation using data from weather stations to forecast PV power output. The best model has been trained using imputed and pre-processed data from 2018-2022 and has shown excellent results. The Gradient Boost algorithm outperformed other algorithms, with an RMSE value 0.808 and 0.921 R2. This suggests that the Gradient Boost algorithm is a promising tool for forecasting PV power output.

关键词： Photovoltaic systems Support vector machines machine learning algorithms Weather forecasting Solar energy Predictive models Data models

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Systematic Analysis of Big Data Based machine learning algorithms on Various Fields

Systematic Analysis of Big Data Based Machine Learning Algor...

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Yang Wang Tianding Zhou Chenglin Li Zhixin Liu Shichuang Zheng Qingqing Liu School of Public Administration Huazhong Agricultural University Wuhan China School of Mechanical and Electronic Engineering Beijing Jiaotong University Beijing China College of Bioinformatics Science and Technology Harbin Medical University Harbin China Department of Information and Mechanical Engineering Jinshan College of Fujian Agriculture and Forestry University Fuzhou China The Department of Electronic Information Engineering Guizhou Vocational Institute of Technology Fuquan China

ISBN: (纸本)9781665470544

machine learning is a basic innovation in foreseeing results in view of data. Four famous Big Data machine learning algorithms are tended to in this paper: Bayesian Decision Theory Classification, and Linear Regression. The underlying two are overseen learning algorithms, the third an independent learning algorithm, and the fourth a relationship algorithm. Advantages of machine learning integrate flexibility and adaptability differentiated and customary biostatistical methods, which makes it deployable for certain tasks, similar to bet partition, finding and gathering, and perseverance assumptions. One more benefit of machine learning algorithms is the capacity to examine assorted data types. Every methodology is broadly explored and examined. Likewise, the accentuation is placed on how the four techniques transaction with one another to rouse investigation of more strong and data-proficient algorithms. At long last, the review portrays the impediments, talks about research difficulties, and recommends future chances to propel the examination on data-proficiency in machine learning.

关键词： Technological innovation machine learning algorithms machine learning Medical services Banking Big Data Partitioning algorithms

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Predicting Missing Values of Well Logs and Classifying Lithology using machine learning algorithms

Predicting Missing Values of Well Logs and Classifying Litho...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Gandhi Rifal Taufik Fuadi Abidin Zaki Fuadi Informatics Department Universitas Syiah Kuala Banda Aceh Indonesia

In this study, we successfully predicted the DT curve's value using the Gradient Boosting Regressor method on well logs data with parameters max depth = 10 and $n$ estimators = 1000. The Mean Square Error (MSE) value of the predicted DT curve for the XI01 well logs data is 1.057887, and the Y08 well logs data is 1.091699. The relatively small MSE value indicated that the DT curve's predicted value is close to the actual value. Using the Norwegian dataset, this study also successfully compared numerous machine learning models in determining lithology categories. The findings show that the Random Forest model has the best F1 value of 0.833, and the Support Vectors machine (SVM) has the worst performance, with an F1 value of 0.736.

关键词： Support vector machines Electrical engineering machine learning algorithms Geology Mean square error methods Boosting Information technology

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Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using machine learning algorithms

Power Curve Estimation of a Wind Turbine Considering Differe...

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International Middle-East Power System Conference, MEPCON

作者： Mostafa Al-Gabalawy Haitham S. Ramadan Mohammed A. Mostafa Shimaa A. Hussien Electronics Communications Computers Engineering Department El-Safawa High Institute of Engineering Cairo Egypt Electrical Power and Machines Dept Faculty of Engineering Zagazig University Zagazig Egypt Electrical Power and Machines Dept Faculty of Engineering Must University 6th October Cairo Egypt Electrical Power and Control Department Pyramids Higher Institute for Engineering and Technology 6th October Cairo Egypt

ISBN: (纸本)9781665463645

Theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. There are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. This paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. There variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. The power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). The RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.

关键词： machine learning algorithms Wind energy Wind speed Estimation Mathematical models Wind turbines Power systems

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Prediction of Dengue using machine learning algorithms: Case Study Dhaka

Prediction of Dengue using Machine Learning Algorithms: Case...

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International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE)

作者： Md. Toyhid Sarwar Md. Al Mamun Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

In recent years, Bangladesh and the South Asian countries are facing a huge problem from mosquito-borne diseases like dengue fever. According to WHO, the global dengue incidence has grown dramatically in recent decades and there are an estimated 100–400 million infections each year. Approximately 100 million people get sick from infection, and 22,000 die from severe dengue. If we can predict the high time of dengue with environmental/meteorological factors then we can take precautionary steps to mitigate the dengue outbreak. The proposed prediction model can accurately predict this epidemic with machine learning algorithms. In this research, environmental factors like rainfall, humidity, temperature of Dhaka city are considered. The proposed machine learning model uses Support Vector machine (SVM) for prediction which performs better than the other models used for comparison. Precision, recall, f1 score, accuracy score are used as the measurement of the performance. We got 97% accuracy in SVM for predicting the dengue epidemic in Bangladesh. Most of the research works in this area have been focused on visualizing the dengue scenario whereas this paper contributes an efficient machine learning model with limited available data for predicting the dengue.

关键词： Support vector machines Temperature measurement Epidemics machine learning algorithms Temperature Urban areas machine learning

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Study of Various machine learning algorithms for use with Automatic Speech Recognition

Study of Various Machine Learning Algorithms for use with Au...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： T. Sathies Kumar T. Sheela D. Arulselvam S. Premalatha K. Srividya Department of EIE Sri Sairam Engineering College Chennai Department of ECE Vinayaka Mission's Kirupananda Variyar Engg. College Salem Department of EEE Sri Sairam Engineering College Chennai

ISBN: (纸本)9781665462761

Speech recognition enables which the system recognize and identify the words from aspeech. It aids in balancing technology enabled Human computer interaction (HCI) and Human robot interaction(HRI). Command and Communication of any information is brought down through Speech. Artificial intellingence carried out with the help of circuit involving speech recognition which involved in Controlling and commanding application such as security system, VCR systems. Speech processing make life easier allows user to perform parallel tasks with increased efficiency and effectiveness(i.e., if the user's hands and eyes are occupied elsewhere OR disabled person who are not using their hand). Occasionally the speech signal is not clearly understand by the system which we communicate with them, by solving this issue we made an various investigation. In this paper we give report of investigate the various methods, algorithm which used in the previous speech recognition technique and system to improve the communication between a Human-Computer Interaction and Human Robotic Interaction to obtain a better results. Various algorithms like dynamic time warping (DTW) and Mel frequency Cepstral coefficients (MFCC), Artificial Neural Network (ANN) and Power Normalized Cepstral Coefficients (PNCC) were analyzed.

关键词： Human computer interaction machine learning algorithms Heuristic algorithms Speech recognition Artificial neural networks Mel frequency cepstral coefficient Task analysis

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An Analysis of Insider Attack Detection Using machine learning algorithms

An Analysis of Insider Attack Detection Using Machine Learni...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： B Nagabhushana Babu M Gunasekaran Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9781665491129

Among the greatest obstacles in cybersecurity is insider threat, which is a well-known massive issue. This anomaly shows that the vulnerability calls for specialized detection techniques, and resources that can help with the accurate and quick detection of an insider who is harmful. Numerous studies on identifying insider threats and related topics were also conducted to tackle this problem are proposed. Various researches sought to improve the conceptual perception of insider risks. Furthermore, there are numerous drawbacks, including a dearth of actual cases, unfairness in drawing decisions, a lack of self-optimization in learning, which would be a huge concern and is still vague, and the absence of an investigation that focuses on the conceptual, technological, and numerical facets concerning insider threats and identifying insider threats from a wide range of perspectives. The intention of the paper is to afford a thorough exploration of the categories, levels, and methodologies of modern insiders based on machine learning techniques. Further, the approach and evaluation metrics for predictive models based on machine learning are discussed. The paper concludes by outlining the difficulties encountered and offering some suggestions for efficient threat identification using machine learning.

关键词： Wireless communication Measurement machine learning algorithms machine learning Predictive models Data models Computer security

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Classification of Lung Cancer by Using machine learning algorithms

Classification of Lung Cancer by Using Machine Learning Algo...

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International Conference on Engineering Technology and their Applications (IICETA)

作者： Joman Al-Tawalbeh Baraa Alshargawi Hiam Alquran Waleed Al-Azzawi Wan Azani Mustafa Ahmed Alkhayyat Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Due to the structure of cancer cells, where the majority of the cells are overlapped with each other, early diagnosis of lung cancer is a difficult challenge, so the cause of lung cancer remains unknown and prevention is difficult, early discovery of lung cancer is the only approach to cure it. The classification of lung cancer is a crucial process, based on signs that appear on patients; cancers could easily be predicted and treated. This paper uses KNN, SVM, Naïve Bayes and narrow neural network (NNN) classifiers. KNN showed 85.87% accuracy. SVM, Naïve Bayes and NNN showed 92.6%, 90.3% and 90% accuracy respectively. Results came after the fact that among the four classifiers SVM was the most accurate and much better to classify our data.

关键词： Support vector machines machine learning algorithms Lung cancer Medical treatment Artificial neural networks

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