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检索条件"主题词=Machine Learning Algorithms"

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Evaluating machine learning algorithms For Bengali Fake News Detection

Evaluating Machine Learning Algorithms For Bengali Fake News...

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International Conference on Computer and Information Technology (ICCIT)

作者： Shafayat Bin Shabbir Mugdha Sayeda Muntaha Ferdous Ahmed Fahmin United International University Dhaka Bangladesh

ISBN: (数字)9781665422444

ISBN: (纸本)9781665446686

In this world of modern technologies and media, online news publications and portals are increasing at a high speed. That is why, nowadays, it has become almost impossible to check out the traditional fact of news headlines and examine them due to the increase in the number of content writers, online media portals, and news portals. Mostly, fake headlines are filled with bogus or misleading content. They attract the commoners by putting phony words or misleading fraudulent content in the headlines to increase their views and share. But, these fake and misleading headlines create havoc in the commoner's life and misguide them in many ways. That is why we took a step so that the commoners can differentiate between fake and real news. We proposed a model that can successfully detect whether the story is fake or accurate based on the news headlines. We created a novel data set of Bengali language and achieved our aim and reached the target using the Gaussian Naive Bayes algorithm. We have used other algorithms, but the Gaussian Naive Algorithm has performed well in our model. This algorithm used a text feature dependent on TF-IDF and an Extra Tree Classifier to choose the attribute. In our model, using Gaussian Naive Bayes we got 87% accuracy which is comparatively best than any other algorithm we used in this model.

关键词： machine learning algorithms Media Feature extraction Classification algorithms Naive Bayes methods Information technology Portals

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Exploring the Application of machine learning algorithms to the City Public Bus Transport

Exploring the Application of Machine Learning Algorithms to ...

引用

Big Data Science and Engineering (BigDataSE), IEEE International Conference on

作者： Michael Alicea Arlen P. McDonald Chunyang Tang Jeong Yang Texas A&M University–San Antonio San Antonio TX USA

ISBN: (数字)9781665403962

ISBN: (纸本)9781665403979

Supervised machine learning is a method that attempts to emulate experienced-based learning similar to how humans learn. This study leverages supervised machine learning algorithms to determine the most important factors of the on-time performance of a San Antonio's VIA bus system using public data sets collected from the 2019 San Antonio Datathon event. A variety of algorithms are used to create models that are capable of predicting on-time performance to see if a bus route met standards on a given day. The algorithms present in the python libraries Sci Kit Learn and Stats Models. Once an algorithm has proven to be highly accurate at predicting the on-time performance, a further study was conducted into the decision-making process of each algorithm to find out which feature it determined to be most important. By knowing the most important feature of the on-time performance, the study gives insight as to what factors affect the performance of bus routes.

关键词： machine learning algorithms Urban areas machine learning Predictive models Prediction algorithms Libraries Standards

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Not Everything You Read Is True! Fake News Detection using machine learning algorithms

Not Everything You Read Is True! Fake News Detection using M...

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Irish Signals and Systems Conference (ISSC)

作者： Vanya Tiwari Ruth G. Lennon Thomas Dowling Letterkenny Institute of Technology Letterkenny Ireland

ISBN: (数字)9781728194189

ISBN: (纸本)9781728194196

This paper considers establishing if a news article is true or if it has been faked. To achieve the task accurately, the work compares different machine learning classification algorithm with the different feature extraction methods. The algorithm with the feature extraction method giving the highest accuracy is then used for future prediction of the labels of news headlines. In this work the algorithm show to have the highest accuracy was logistic regression with 71% percent accuracy when used with tf-idf feature extraction method.

关键词： Social network services Feature extraction machine learning algorithms Classification algorithms Media Task analysis Data models

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Software Defects Prediction using machine learning algorithms

Software Defects Prediction using Machine Learning Algorithm...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Marwa Assim Qasem Obeidat Mustafa Hammad University of Bahrain Sakheer Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Software development and the maintenance life cycle are lengthy processes. However, the possibility of having defects in the software can be high. Software reliability and performance are essential measures of software success, which affects user satisfaction and software cost. Predicting software defects using machine learning (ML) algorithms is one approach in this direction. Implementing this approach in the earlier stages of the software development improves software performance quality and reduces software maintenance cost. Different models and techniques have been implemented in many studies to predict software defects. This investigation implements ML algorithms, such as artificial neural networks (ANNs), random forest (RF), random tree (RT), decision table (DT), linear regression (LR), gaussian processes (GP), SMOreg, and M5P. A new software defect prediction model for software future defect prediction is proposed. The defect prediction is based on historical data. The results showed that a combination of ML algorithms could be used effectively to predict software defects. The SMOreg classifier scored the best performance results, where the ANN classifier scores the worst results.

关键词： Software Prediction algorithms Software algorithms machine learning algorithms Classification algorithms Predictive models Measurement

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A Comparative Study of Predictive machine learning algorithms for COVID-19 Trends and Analysis

A Comparative Study of Predictive Machine Learning Algorithm...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ajinkya Kunjir Dishant Joshi Ritika Chadha Tejas Wadiwala Vikas Trikha Lakehead University Thunder Bay Canada

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

This paper attempts to conduct analysis on the WHO dataset to produce predictive analysis applying different machine learning regression approaches such as decision trees, LSTM, and CNN regressor. The primary data has 91 entries, which consists of data of various countries with respect to dates along with confirmed cases, confirmed deaths, and recovered cases. The dataset has been divided into 70:30 in which 70 percent is used for training and validation, and 30 percent is used for testing. The coronavirus disease outbreak started in 2019, arising in Wuhan, China. The key objective is to exercise different artificial intelligence approaches, we ought to predict the confirmed cases, confirmed deaths, and recovered cases, and further, various visualization techniques have been used to deduce the meaningful inferences from the model's prediction and perform specific analytics on the results concluded. The prediction models such as LSTM and CNN are evaluated on the basis of several loss functions such as R2 score and Mean Squared Error.

关键词： COVID-19 Training machine learning algorithms Predictive models Market research Real-time systems Regression tree analysis

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Monthly Rainfall Prediction Using Various machine learning algorithms for Early Warning of Landslide Occurrence

Monthly Rainfall Prediction Using Various Machine Learning A...

引用

Emerging Technology (INCET), International Conference for

作者： Shikha Srivastava Nishchay Anand Sumit Sharma Sunil Dhar Lokesh Kumar Sinha Defence Terrain Research Laboratory Defence Research and Development Organization New Delhi India Electrical and Electronics Maharaja Agrasen Institute of Technology New Delhi India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Landslides are considered to be calamitous natural hazards commonly recurring in the Indian Himalayas. Majority of landslides are induced by prolonged or heavy rainfall. Rainfall forecasting helps in identifying the precipitation conditions responsible for landslide occurrence. The proposed research work provides the performance comparison of various machine learning algorithms such as linear regression, back propagation neural network (BPNN), support vector regression (SVR) and long short term memory network (LSTM) used to forecast rainfall which can be compared with the rainfall thresholds to predict landslide occurrence. The analysis is performed using antecedent rainfall data obtained from Narendra Nagar, a small town in the Tehri Garhwal district of Uttarakhand for the period of 1901-2015. Owing to the limited predictability of instantaneous state of the weather, daily rainfall observations are aggregated into monthly indexes. The proposed algorithms use preprocessing techniques followed by data normalization to increase the accuracy of forecasting models. The developed models have the ability to predict rainfall intensity one month in advance or for a specific month of the upcoming year depending upon the dataset used. The study concludes that the BPNNs are able to outperform and provide optimal inferences stating the aptness of artificial neural networks (ANNs) in estimating rainfall and hence predicting the possibility of landslide occurrence well in advance. The study is conducted explicitly for regions highly vulnerable to landslides near Narendra Nagar but may be implemented to any landslide prone area.

关键词： Terrain factors Forecasting Support vector machines Predictive models machine learning algorithms Linear regression Artificial neural networks

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Graph and Natural Language Processing Based Recommendation System for Choosing machine learning algorithms

Graph and Natural Language Processing Based Recommendation S...

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International Conference on Advanced Infocomm Technology (ICAIT)

作者： Y. Mahima T.N.D.S. Ginige University of Westminster Colombo Sri Lanka Universal College Lanka Colombo Sri Lanka

ISBN: (数字)9781728183848

ISBN: (纸本)9781728183855

machine learning is a subset of Artificial Intelligence(AI). It provides the systems with the ability to learn automatically and perform independently without being programmed. When concentrating on the machine learning project choosing an algorithm is one of the most important steps. machine learning algorithms are the math and logic that adjust the training of the model and the performance. When it comes to a machine learning project the choosing a correct algorithm for implementing the model is a big task. If the developer doesn't choose the suitable and most efficient algorithm for the program the accuracy of the program can be decreased. When we look at these machine learning algorithms there is no solution or no approach that fits for all. Several factors affect when choosing a machine learning algorithm such as the number of data, type of the problem ..etc. Most of the time a developer chooses the machine learning algorithm using his prior experience or analyzing several past similar projects. However, when it comes to a beginner it can be a tough experience. Most of the time beginners try with several algorithmic approaches to implement their model without any understanding. However, to avoid this there is no proper solution. Therefore here in this research, proposed an NLP and graph analytics-based approach for recommending a machine learning algorithm for a project. The summary of the proposed solution is here it analyzes past algorithms used in machine learning projects which are stored in a graph using NLP based keyword analysis and recommend the most suitable algorithmic approach. When the user inputs his project idea by using the Natural Language processing it generates keywords for the project description. Thereafter the system analyzes the graph which stores past machine learning projects and used technologies to find the most suitable algorithmic approach to the users' project. Moreover, it shows how the proposed algorithms were used in past similar

关键词： machine learning algorithms machine learning Prediction algorithms Natural language processing Industries Classification algorithms Software algorithms

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Analyzing machine learning algorithms for Sentiments in Arabic Text

Analyzing Machine Learning Algorithms for Sentiments in Arab...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ayman Yafoz Malek Mouhoub University of Regina Regina Canada

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

The studies addressing the application of machine and deep learning models to analyze the sentiments of Arabic online reviews related to the real-estate and automobile fields are not mature. To fill this gap, this research has focused on classifying three types of sentiments in Arabic real-estate and automobile online reviews, which are negative, positive, and mixed sentiments. The research focused on analyzing the reviews written in both Gulf Cooperation Council (GCC) dialects and modern standard Arabic (MSA). The research also explained the natural language processing strategies that were adopted to prepare the text for classification. The research discussed the details of collecting and annotating the data, preprocessing procedures, and feature selection methods. Following this, the research highlighted the adopted strategies for balancing and splitting the datasets, and it showed the analysis of the classification results for both machine and deep learning models. Finally, the suggestions for future work were provided in this research.

关键词： Deep learning Analytical models machine learning algorithms Feature extraction Natural language processing Automobiles Standards

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Fall-detection on a wearable micro controller using machine learning algorithms

Fall-detection on a wearable micro controller using machine ...

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International Conference on Smart Computing (SMARTCOMP)

作者： Lena Oden Thorsten Witt FernUniversität in Hagen

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

Wearables providing fall detection can provide faster emergency services for elderly, yet privacy concerns limit acceptance of this technology. In this work, we evaluate a machine learning algorithm, called Bosnai, for embedded edge devices to detect falls. The prototype is Arduino based and can be integrated into fabrics for clothes, belts, or other accessories. The fall detection is performed offline on the device. We used data from public datasets of movement and fall events to train a tree-based machine learning model. We evaluated different combinations of prepossessed parameters as input features for the learning algorithm. The learned model is transferred to the microcontroller and can classify the sensor data offline but in real-time. We evaluate the performance of our device by performing intensive test runs with the prototype. The microcontroller is extremely limited in terms of memory capacity and computing performance, which only allows a limited number of features for learning. For this reason, it is especially important to preprocess the raw accelerator data and select the right features for training and inference. Our results show that the best performance (approx. 94.2 % accuracy) is achieved when we choose absolute acceleration and variance as features, with a sampling rate of 20 Hz and a recording window of 3s, as this system is the most robust against external interference.

关键词： Performance evaluation Training machine learning algorithms Microcontrollers Wearable computers Data models Fall detection

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Prediction and Analysis of Permanent Magnet Synchronous Motor parameters using machine learning algorithms

Prediction and Analysis of Permanent Magnet Synchronous Moto...

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International Conference on Advances in Electronics, Computers and Communications (ICAECC)

作者： Rushank Savant Abhiram Ajith Kumar Aditya Ghatak School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781728191836

ISBN: (纸本)9781728180458

The widespread acceptance of PMSM as the motor of choice for electric vehicles, along with various other applications demands the need of stringent monitoring of temperature in order to avoid increased temperatures. Temperature values beyond a specific range can lead to major operational problems in Permanent Magnet Synchronous Motor(PMSM) along with additional maintenance costs. Using r2 values this paper compares the performance of three different machine learning algorithms in the estimation of parameters in a Permanent Magnet Synchronous Motor. Making use of a pre existing test set, the accuracies in predictions by the following models are compared: Support Vector Regressor, Random Forest Regressor and Polynomial Regression. Random Forest Regression shows the highest r2 values(statistical way of knowing variation of dependent variables explained by independent variables for a particular regression model) which proves the accuracy of the model.

关键词： Temperature measurement machine learning algorithms Estimation Predictive models Permanent magnet motors Synchronous motors Random forests

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