scholarly journals Identifying Tree-Related Microhabitats in TLS Point Clouds Using Machine Learning

2018 ◽  
Vol 10 (11) ◽  
pp. 1735 ◽  
Author(s):  
Nataliia Rehush ◽  
Meinrad Abegg ◽  
Lars Waser ◽  
Urs-Beat Brändli
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Laser Scanning ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Forest Monitoring ◽  
Geometric Features ◽  
Automated Identification ◽  
Learning Techniques ◽  
Close Range

Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention in ecosystem conservation, forest management and research. However, TreMs have until now only been assessed by experts during field surveys, which are time-consuming and difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different TreMs (bark, bark pockets, cavities, fungi, ivy and mosses) in dense TLS point clouds using machine learning algorithms, including deep learning. To classify the TreMs, we applied: (1) the Random Forest (RF) classifier, incorporating frequently used local geometric features and two additional self-developed orientation features, and (2) a deep Convolutional Neural Network (CNN) trained using rasterized multiview orthographic projections (MVOPs) containing top view, front view and side view of the point’s local 3D neighborhood. The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized MVOPs are even more suitable. Whereas the overall accuracy of the RF was 70%, that of the deep CNN was substantially higher (83%). This study reveals that close-range TLS is promising for the semi-automated identification of TreMs for forest monitoring purposes, in particular when applying deep learning techniques.

scholarly journals AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 843-849
Author(s):  
E. Özdemir ◽  
F. Remondino ◽  
A. Golkar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Geometric Features ◽  
Semantic Classes ◽  
3D Point Clouds ◽  
City Models

Abstract. With recent advances in technology, 3D point clouds are getting more and more frequently requested and used, not only for visualization needs but also e.g. by public administrations for urban planning and management. 3D point clouds are also a very frequent source for generating 3D city models which became recently more available for many applications, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are generated have been evolving towards automation with different approaches. As most of these approaches are based on point clouds with proper semantic classes, our aim is to classify aerial point clouds into meaningful semantic classes, e.g. ground level objects (GLO, including roads and pavements), vegetation, buildings’ facades and buildings’ roofs. In this study we tested and evaluated various machine learning algorithms for classification, including three deep learning algorithms and one machine learning algorithm. In the experiments, several hand-crafted geometric features depending on the dataset are used and, unconventionally, these geometric features are used also for deep learning.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

scholarly journals Deep learning models for classification of gases detected by sensor arrays of artificial nose

2019 ◽  
Author(s):  
Ismael Araujo ◽  
Juan Gamboa ◽  
Adenilton Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Classification Problems ◽  
Artificial Nose ◽  
Learning Techniques

To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

scholarly journals Deep learning and model personalization in sensor-based human activity recognition

2022 ◽  
Author(s):  
Anna Ferrari ◽  
Daniela Micucci ◽  
Marco Mobilio ◽  
Paolo Napoletano
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Wearable Sensors ◽  
Human Activity Recognition ◽  
Population Diversity ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques

AbstractHuman activity recognition (HAR) is a line of research whose goal is to design and develop automatic techniques for recognizing activities of daily living (ADLs) using signals from sensors. HAR is an active research filed in response to the ever-increasing need to collect information remotely related to ADLs for diagnostic and therapeutic purposes. Traditionally, HAR used environmental or wearable sensors to acquire signals and relied on traditional machine-learning techniques to classify ADLs. In recent years, HAR is moving towards the use of both wearable devices (such as smartphones or fitness trackers, since they are daily used by people and they include reliable inertial sensors), and deep learning techniques (given the encouraging results obtained in the area of computer vision). One of the major challenges related to HAR is population diversity, which makes difficult traditional machine-learning algorithms to generalize. Recently, researchers successfully attempted to address the problem by proposing techniques based on personalization combined with traditional machine learning. To date, no effort has been directed at investigating the benefits that personalization can bring in deep learning techniques in the HAR domain. The goal of our research is to verify if personalization applied to both traditional and deep learning techniques can lead to better performance than classical approaches (i.e., without personalization). The experiments were conducted on three datasets that are extensively used in the literature and that contain metadata related to the subjects. AdaBoost is the technique chosen for traditional machine learning, while convolutional neural network is the one chosen for deep learning. These techniques have shown to offer good performance. Personalization considers both the physical characteristics of the subjects and the inertial signals generated by the subjects. Results suggest that personalization is most effective when applied to traditional machine-learning techniques rather than to deep learning ones. Moreover, results show that deep learning without personalization performs better than any other methods experimented in the paper in those cases where the number of training samples is high and samples are heterogeneous (i.e., they represent a wider spectrum of the population). This suggests that traditional deep learning can be more effective, provided you have a large and heterogeneous dataset, intrinsically modeling the population diversity in the training process.

scholarly journals Airline Delay Prediction using Machine Learning and Deep Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 1049-1054
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Short Term Memory ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Maximum Delay ◽  
Learning Techniques ◽  
Delay Prediction

In this paper, we have tried to predict flight delays using different machine learning and deep learning techniques. By using such a model it can be easier to predict whether the flight will be delayed or not. Factors like ‘WeatherDelay’, ‘NASDelay’, ‘Destination’, ‘Origin’ play a vital role in this model. Using machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), the f1-score, precision, recall, support and accuracy have been predicted. To add to the model, Long Short-Term Memory (LSTM) RNN architecture has also been employed. In the paper, the dataset from Bureau of Transportation Statistics (BTS) of the ‘Pittsburgh’ is being used. The results computed from the above mentioned algorithms have been compared. Further, the results were visualized for various airlines to find maximum delay and AUC-ROC curve has been plotted for Random Forest Algorithm. The aim of our research work is to predict the delay so as to minimize loses and increase customer satisfaction.

scholarly journals Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

2021 ◽  
Vol 12 (3) ◽  
pp. 4396-4405
Author(s):  
R Kanthavel Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Elastic Tissue ◽  
Learning Approaches ◽  
X Rays ◽  
Imaging Analysis ◽  
Bone Injury ◽  
Learning Techniques

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.

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.

scholarly journals ALS POINT CLOUD CLASSIFICATION USING POINTNET++ AND KPCONV WITH PRIOR KNOWLEDGE

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 91-96
Author(s):  
M. Kada ◽  
D. Kuramin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Prior Knowledge ◽  
Laser Scanning ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Software Applications ◽  
Point Cloud Classification

Abstract. In the practical and professional work of classifying airborne laser scanning (ALS) point clouds, there are nowadays numerous methods and software applications available that are able to separate the points into a few basic categories and do so with a known and consistent quality. Further refinement of the classes then requires either manual or semi-automatic work, or the use of supervised machine learning algorithms. In using supervised machine learning, e.g. Deep Learning neural networks, however, there is a significant chance that they will not maintain the approved quality of an existing classification. In this study, we therefore evaluate the application of two neural networks, PointNet++ and KPConv, and propose to integrate prior knowledge from a pre-existing classification in the form of height above ground and an encoding of the already available labels as additional per-point input features. Our experiments show that such an approach can improve the quality of the 3D classification results by 6% to 10% in mean intersection over union (mIoU) depending on the respective network, but it also cannot completely avoid the aforementioned problems.

scholarly journals Performance Study of N-grams in the Analysis of Sentiments

2021 ◽  
pp. 477-483
Author(s):  
O. E. Ojo ◽  
A. Gelbukh ◽  
H. Calvo ◽  
O. O. Adebanji
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Study ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Economic Texts ◽  
Economic Domain

In this work, a study investigation was carried out using n-grams to classify sentiments with different machine learning and deep learning methods. We used this approach, which combines existing techniques, with the problem of predicting sequence tags to understand the advantages and problems confronted with using unigrams, bigrams and trigrams to analyse economic texts. Our study aims to fill the gap by evaluating the performance of these n-grams features on different texts in the economic domain using nine sentiment analysis techniques and found more insights. We show that by comparing the performance of these features on different datasets and using multiple learning techniques, we extracted useful intelligence. The evaluation involves assessing the precision, recall, f1-score and accuracy of the function output of the several machine learning algorithms proposed. The methods were tested using Amazon, IMDB, Reuters, and Yelp economic review datasets and our comprehensive experiment shows the effectiveness of n-grams in the analysis of sentiments.

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