Classical Machine Learning Techniques in the Search of Extrasolar Planets

The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.

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OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques

Database ◽

10.1093/database/baaa067 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Fabio R. Cerqueira ◽

Ana Tereza Ribeiro Vasconcelos

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Open Reading Frames ◽

Machine Learning Techniques ◽

Bacterial Genomes ◽

Small Proteins ◽

Learning Techniques ◽

Speed Up ◽

Computational Procedures ◽

Small Orfs

Abstract Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

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Animal species classification using machine learning techniques

MATEC Web of Conferences ◽

10.1051/matecconf/201927702033 ◽

2019 ◽

Vol 277 ◽

pp. 02033

Author(s):

Fahad Alharbi ◽

Abrar Alharbi ◽

Eiji Kamioka

Keyword(s):

Machine Learning ◽

Feature Detection ◽

Animal Species ◽

Machine Learning Techniques ◽

Species Classification ◽

Research Areas ◽

Learning Techniques ◽

Multiple Feature ◽

The Face ◽

Great Danger

Animals recognition is one of the research areas in which few effective technologies have been proposed, especially in the predator animals' domain. Predator animals present a great danger to people who are camping or staying in outdoor areas and they are also a menace to livestock. In this paper, a multiple feature detection of predator animals is proposed. This method focuses on the face of the animal, explicitly the eyes and the ears. A database was created by collecting the features of ears and eyes from 10 animals and an experiment was conducted using machine learning techniques such as SVM and MLP to classify them as predators or pets. The evaluation results achieved the classification accuracies of 82% for MLP and 78% for SVM, which justify its effectiveness for the proposed method.

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Image Processing and Machine Learning Techniques for Facial Expression Recognition

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications ◽

10.4018/978-1-60566-314-2.ch016 ◽

2009 ◽

pp. 247-262 ◽

Cited By ~ 2

Author(s):

Anastasios Koutlas ◽

Dimitrios I. Fotiadis

Keyword(s):

Machine Learning ◽

Image Processing ◽

Facial Expression ◽

Facial Expression Recognition ◽

Machine Learning Techniques ◽

Automatic Identification ◽

Expression Recognition ◽

Basic Emotions ◽

Learning Techniques ◽

The Face

The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented: Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral); and finally, the future trends towards automatic facial expression recognition are described.

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Survey on Civil Complaints Management System by Using Machine Learning Techniques

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1449 ◽

2021 ◽

pp. 606-610

Author(s):

Sayali Bhosale ◽

Sonali Patankar ◽

Kshitija Kadam ◽

Rujuta Dhere ◽

Prof. Manisha Desai

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Clustering Method ◽

Registration System ◽

Handling System ◽

Complaint Handling ◽

Learning Techniques ◽

Framework Model ◽

Speed Up ◽

The Government

Today’s Government Complaints Registration System is totally Human Operable hence it consumes lot of time to resolve each of those complaint. The proposed system is being developed for the government offices to create a helpful complaint registering platform which will be efficient. The Framework model of civil complaint handling system is done by using sentimental analysis and ML techniques to speed up the process of categorization and prioritization of complaints. The system will analyses the citizen sentiment to prioritize the complaints. Later then the categorization of complaints done by using clustering method and give them priority based upon urgency of each one respectively. The municipality can use this method to identify citizen’s needs and estimate their satisfaction.

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Automatic Attendance System with Face Recognition using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d9516.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 3642-3646

Keyword(s):

Machine Learning ◽

Face Recognition ◽

Recognition Algorithm ◽

Machine Learning Techniques ◽

Project Work ◽

Data Set ◽

Second Stage ◽

Learning Techniques ◽

The Face ◽

Face Detection And Recognition

Object and Face detection and recognition is one of the mostly researched area in computer vision. This particular field of work is widely used in mobile phones and laptops for unlocking the system by the user. Recently this field gained importance in the automatic attendance system in schools, colleges and institution. The institutions are moving from biometric based attendance to face recognition based attendance system. In this project work, I have used machine learning techniques to create a complete system of automatic attendance system which can be implemented very easily. There are majorly four steps involved in the system. Firstly, the datasets can be created instantly using webcam and in the second stage the created data set have to be trained and the trainer algorithm will create the trainer.yml document. As a next step, the face recognition algorithm have to be performed in order to recognize the faces of various students and teacher. In the final step, the attendance of the students will be updated in the CSV file or Excel. The proposed work is very much suited for the real time applications like automatic attendance system. HaarCascade is very eff

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Domain adaptation-based transfer learning using adversarial networks

The Knowledge Engineering Review ◽

10.1017/s0269888920000107 ◽

2020 ◽

Vol 35 ◽

Cited By ~ 1

Author(s):

Farzaneh Shoeleh ◽

Mohammad Mehdi Yadollahi ◽

Masoud Asadpour

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Transfer Learning ◽

Domain Adaptation ◽

Machine Learning Techniques ◽

Implicit Assumption ◽

Adversarial Networks ◽

Learning Techniques ◽

Speed Up ◽

Similar Domain

Abstract There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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