Machine Learning Algorithms

Author(s):  
Namrata Dhanda ◽  
Stuti Shukla Datta ◽  
Mudrika Dhanda

Human intelligence is deeply involved in creating efficient and faster systems that can work independently. Creation of such smart systems requires efficient training algorithms. Thus, the aim of this chapter is to introduce the readers with the concept of machine learning and the commonly employed learning algorithm for developing efficient and intelligent systems. The chapter gives a clear distinction between supervised and unsupervised learning methods. Each algorithm is explained with the help of suitable example to give an insight to the learning process.

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.


2021 ◽  
Author(s):  
Yingxian Liu ◽  
Cunliang Chen ◽  
Hanqing Zhao ◽  
Yu Wang ◽  
Xiaodong Han

Abstract Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is underground sampling. However, not every well has samples due to technical reasons such as excessive well deviation or high cost during the exploration stage. Therefore, analogies or empirical formulas have to be adopted to carry out research in many cases. But a large number of oilfield developments have shown that the errors caused by these methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more extensive. This paper proposed a method for predicting crude oil physical properties based on machine learning algorithms. This method uses PVT data from nearly 100 wells in Bohai Oilfield. 75% of the data is used for training and learning to obtain the prediction model, and the remaining 25% is used for testing. Practice shows that the prediction results of the machine learning algorithm are very close to the actual data, with a very small error. Finally, this method was used to apply the preliminary plan design of the BZ29 oilfield which is a new oilfield. Especially for the unsampled sand bodies, the fluid physical properties prediction was carried out. It also compares the influence of the analogy method on the scheme, which provides potential and risk analysis for scheme design. This method will be applied in more oil fields in the Bohai Sea in the future and has important promotion value.


The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area


InterConf ◽  
2021 ◽  
pp. 393-403
Author(s):  
Olexander Shmatko ◽  
Volodimir Fedorchenko ◽  
Dmytro Prochukhan

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.


Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.


2022 ◽  
pp. 34-46
Author(s):  
Amtul Waheed ◽  
Jana Shafi ◽  
Saritha V.

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.


2020 ◽  
Author(s):  
Alex J. C. Witsil

Volcanoes are dangerous and complex with processes coupled to both the subsurface and atmosphere. Effective monitoring of volcanic behavior during and in between periods of crisis requires a diverse suite of instruments and processing routines. Acoustic microphones and video cameras are typical in long-term deployments and provide important constraints on surficial and observational activity yet are underutilized relative to their seismic counterpart. This dissertation increases the utility of infrasound and video datasets through novel applications of computer vision and machine learning algorithms, which help constrain source dynamics and track shifts in activity. Data analyzed come from infrasound and camera installations at Stromboli Volcano, Italy and Villarrica Volcano, Chile and are diverse in terms of the recorded activity. At Villarrica, a computer vision algorithm quantifies video data into a set of characteristic features that are used in a multiparametric analysis with seismic and infrasound data to constrain activity during a period of crisis in 2015. Video features are also input into a machine learning algorithm that classifies data into five modes of activity, which helps track behavior over weekly and monthly time scales. At Stromboli, infrasound signals radiating from the multiple active vents are synthesized into characteristic features and then clustered via an unsupervised learning algorithm. Time histories of cluster activity at each vent reveal concurrent shifts in behavior that suggest a linked plumbing system between the vents. The algorithms presented are general and modular and can be implemented at monitoring agencies that already collect acoustic and video data.


2020 ◽  
Vol 17 (9) ◽  
pp. 4294-4298
Author(s):  
B. R. Sunil Kumar ◽  
B. S. Siddhartha ◽  
S. N. Shwetha ◽  
K. Arpitha

This paper intends to use distinct machine learning algorithms and exploring its multi-features. The primary advantage of machine learning is, a machine learning algorithm can predict its work automatically by learning what to do with information. This paper reveals the concept of machine learning and its algorithms which can be used for different applications such as health care, sentiment analysis and many more. Sometimes the programmers will get confused which algorithm to apply for their applications. This paper provides an idea related to the algorithm used on the basis of how accurately it fits. Based on the collected data, one of the algorithms can be selected based upon its pros and cons. By considering the data set, the base model is developed, trained and tested. Then the trained model is ready for prediction and can be deployed on the basis of feasibility.


2020 ◽  
Vol 4 (1) ◽  
pp. 117-122
Author(s):  
Agus Ambarwari ◽  
Qadhli Jafar Adrian ◽  
Yeni Herdiyeni

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.


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