scholarly journals Animal species classification using machine learning techniques

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.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals Video spam comment features selection using machine learning techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 1046
Author(s):  
Nabilah Alias ◽  
Cik Feresa Mohd Foozy ◽  
Sofia Najwa Ramli ◽  
Naqliyah Zainuddin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
Feature Detection ◽  
Random Tree ◽  
Machine Learning Techniques ◽  
Decision Table ◽  
Features Selection ◽  
Video Sharing ◽  
Learning Techniques

<p>Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.</p>

scholarly journals Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data

2021 ◽  
Vol 13 (19) ◽  
pp. 3983
Author(s):  
Emanuele Pontoglio ◽  
Paolo Dabove ◽  
Nives Grasso ◽  
Andrea Maria Lingua
Keyword(s):  
Machine Learning ◽  
Feature Detection ◽  
Point Clouds ◽  
Automatic Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Multispectral Data ◽  
Learning Techniques ◽  
Gravel Bars ◽  
Fluvial Environments

The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and anthropic issues. The developed methodology was applied considering the acquisition of multiple photogrammetric images thanks to unmanned aerial vehicles (UAV) carrying multispectral cameras. These surveys were carried out in the Salbertrand area, along the Dora Riparia River, situated in Piedmont (Italy). The authors developed an algorithm able to identify and detect the water table contour concerning the landed areas: the automatic classification in ML found a valid identification of different patterns (water, gravel bars, vegetation, and ground classes) in specific hydraulic and geomatics conditions. Indeed, the RE+NIR data gave us a sharp rise in terms of accuracy by about 11% and 13.5% of F1-score average values in the testing point clouds compared to RGB data. The obtained results about the automatic classification led us to define a new procedure with precise validity conditions.

Overview of Machine Learning Approaches for Wireless Communication

2019 ◽  
pp. 123-140 ◽  
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

Image Processing and Machine Learning Techniques for Facial Expression Recognition

2009 ◽  
pp. 247-262 ◽  
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.

scholarly journals Machine Learning Techniques for MultiAgent Systems

2017 ◽  
Author(s):  
Yoad Lewenberg
Keyword(s):  
Machine Learning ◽  
Multiagent Systems ◽  
Language Processing ◽  
Nash Equilibria ◽  
Cooperative Game Theory ◽  
Graphical Model ◽  
Machine Learning Techniques ◽  
Research Areas ◽  
Learning Techniques ◽  
Interest In Research

Research in artificial intelligence ranges over many subdisciplines, such as Natural Language Processing, Computer Vision, Machine Learning, and MultiAgent Systems. Recently, AI techniques have become increasingly robust and complex, and there has been enhanced interest in research at the intersection of seemingly disparate research areas. Such work is motivated by the observation that there is actually a great deal of commonality among areas, that can be exploited within subfields. One example of a successful combination is the intersection of machine learning and multiagent systems. For example ,Kearns et al. [2001] proposed an efficient graphical model-based algorithm for calculating Nash equilibria. Going in the other direction, Datta et al. [2015] showed that solution concepts from cooperative game theory can be used to uniquely characterize the influence measure of classifiers.

scholarly journals Lane Detection Based on Connection of Various Feature Extraction Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mingfa Li ◽  
Yuanyuan Li ◽  
Min Jiang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Feature Detection ◽  
Extraction Methods ◽  
Lane Detection ◽  
New Method ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques

Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multifeature detection problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification rather than feature design. But modern machine learning methods can be used to identify the features that are rich in recognition and have achieved success in feature detection tests. However, these methods have not been fully implemented in the efficiency and accuracy of lane detection. In this paper, we propose a new method to solve it. We introduce a new method of preprocessing and ROI selection. The main goal is to use the HSV colour transformation to extract the white features and add preliminary edge feature detection in the preprocessing stage and then select ROI on the basis of the proposed preprocessing. This new preprocessing method is used to detect the lane. By using the standard KITTI road database to evaluate the proposed method, the results obtained are superior to the existing preprocessing and ROI selection techniques.

An Efficient Action Detection Model Using Deep Belief Networks

2019 ◽  
Vol 16 (8) ◽  
pp. 3232-3236
Author(s):  
L. K. Joshila Grace ◽  
K. Rahul ◽  
P. S. Sidharth
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Action Recognition ◽  
Machine Learning Techniques ◽  
Action Detection ◽  
Detection Model ◽  
Research Areas ◽  
Learning Techniques ◽  
Bounding Boxes ◽  
Living Being

Computer Vision and image processing have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Action detection and Pattern Recognition. Action recognition is a new advancement of pattern recognition approaches where the actions performed by any action or living being is tracked and monitored. Action recognition still encounters some challenges that needs to be looked upon and perform recognize the actions is a very minimal time. Networks like SVM and Neural Networks are used to train the network in such a way they are able to detect a pattern of an action when a new frame is given. In this paper, we have proposed a model which detects patterns of actions from a video or an image. Bounding boxes are used to detect the actions and localize it. Deep Belief Network is used to train the model where numerous images having actions are given as the training set. The performance evaluation was done on the model and it is observed that it detects the actions very accurately when a new image is given to the network.

scholarly journals Automatic Attendance System with Face Recognition using Machine Learning

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

scholarly journals Determining Research Priorities for Astronomy Using Machine Learning

2022 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Brian Thomas ◽  
Harley Thronson ◽  
Anthony Buonomo ◽  
Louis Barbier
Keyword(s):  
Machine Learning ◽  
Latent Dirichlet Allocation ◽  
Research Priorities ◽  
Machine Learning Techniques ◽  
Annual Growth Rate ◽  
Compound Annual Growth Rate ◽  
Research Areas ◽  
Learning Techniques ◽  
Scientific Papers ◽  
Preliminary Version

Abstract We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high-impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey. One metric is based on a sum of the fractional contribution to each topic by all scientific papers (“counts”) while the other is the Compound Annual Growth Rate of counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al.

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