scholarly journals Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

2021 ◽  
Vol 13 (8) ◽  
pp. 1410
Author(s):  
Stefania Amici ◽  
Alessandro Piscini
Keyword(s):  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Spectral Features ◽  
Visual Interpretation ◽  
Band Ratio ◽  
Detection Techniques ◽  
Space Agency ◽  
South Wales ◽  
Italian Space Agency

Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

scholarly journals ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 215-222
Author(s):  
D. Spiller ◽  
L. Ansalone ◽  
S. Amici ◽  
A. Piscini ◽  
P. P. Mathieu
Keyword(s):  
Near Infrared ◽  
Fire Detection ◽  
Spectral Signature ◽  
Support Vector ◽  
Band Ratio ◽  
Space Agency ◽  
South Wales ◽  
Italian Space Agency ◽  
Informative Content ◽  
Shortwave Infrared

Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Mis­sione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be com­pared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

scholarly journals A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

2018 ◽  
Vol 7 (8) ◽  
pp. 223 ◽  
Author(s):  
Zhidong Zhao ◽  
Yang Zhang ◽  
Yanjun Deng
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Support Vector ◽  
Visual Interpretation ◽  
Adaptive Boosting ◽  
Original Dataset

Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

Soft computing based audio signal analysis for accident prediction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hima Bindu Valiveti ◽  
Anil Kumar B. ◽  
Lakshmi Chaitanya Duggineni ◽  
Swetha Namburu ◽  
Swaraja Kuraparthi
Keyword(s):  
Feature Extraction ◽  
Audio Signal ◽  
Support Vector ◽  
Classification Algorithms ◽  
Spectral Features ◽  
Road Accidents ◽  
Content Type ◽  
Detection Techniques ◽  
Car Crashes ◽  
High Level

Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques. Findings Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested. Practical implications Denoising of the audio samples for perfect feature extraction was a tedious chore. Originality/value The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

scholarly journals Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3198 ◽  
Author(s):  
Victor Garcia-Font ◽  
Carles Garrigues ◽  
Helena Rifà-Pous
Keyword(s):  
Machine Learning ◽  
Sensor Networks ◽  
Smart City ◽  
Smart Cities ◽  
Technical Difficulty ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques ◽  
Complex Scenario

Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments.

scholarly journals Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning

2021 ◽  
Vol 13 (17) ◽  
pp. 3459
Author(s):  
Joanna Pranga ◽  
Irene Borra-Serrano ◽  
Jonas Aper ◽  
Tom De Swaef ◽  
An Ghesquiere ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Data ◽  
Perennial Ryegrass ◽  
Plant Traits ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Spectral Features ◽  
Least Squares Regression ◽  
Herbage Yield

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.

scholarly journals Design and Implementation of Electromyography (EMG) based Real-Time Pattern Recognition model for Prosthetic hand Control

2021 ◽  
Author(s):  
Pasan Yashoda Jayaweera
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Extraction Techniques ◽  
Time Pattern ◽  
Feed Forward ◽  
Prosthetic Devices ◽  
Detection Techniques

Surface electromyogram (EMG) signals are a key component in myoelectric control systems utilized in modern prosthetic devices. Despite extensive study into EMG gesture detection techniques for hand and arm gestures, most prosthetic devices rely on direct control approaches that are often confined to single movements. The purpose of this paper is to investigate various feature extraction techniques and to compare various machine learning algorithms, window sizes to identify the most suitable algorithm, window size for real-time gesture recognition. For this purpose, a publicly available pre-labeled 2-channel EMG dataset was used as EMG signals. Feature sets for each window size were extracted using various feature extraction techniques and fed into support vector machines, k-nearest neighbors, ensemble learning, and feed-forward artificial neural network (ANN) classifiers. The feed-forward neural networks classifier was determined to be the best classifier based on its accuracies, sizes, and prediction delays for each window size. The maximum accuracy of the feed-forward ANN classifier was ≈87% with a 300-millisecond window size. the use of the majority voting technique was considered in terms of the number of votes and the window sizes.

Vnir reflectance spectroscopyof Mercury like glasses

2021 ◽  
Author(s):  
Enrico Bruschini ◽  
Cristian Carli ◽  
Andreas Morlok ◽  
Fabrizio Capaccioni ◽  
Aleksandra Stojic ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Chemical Compositions ◽  
Spectral Features ◽  
Ftir Microscopy ◽  
Space Agency ◽  
Dominant Feature ◽  
Clear Band ◽  
Glassy Materials ◽  
Italian Space Agency ◽  
Reflectance Ftir

<p>Glassy materials have been recognized over Mars, Moon and many different meteorites (Farrand et al. 2016; Delano 1986; Varela & Kurat 2004). Planetary glasses result from impact events but they are also found as volcanic products (Farrand et al 2016). Morlock et al. (2017) and Morlok et al. (2021) investigated by means of different experimental techniques (bi-directional diffuse reflectance FTIR, in situ FTIR microscopy, Raman, EPMA and optical microscopy) a suite of synthetic samples with composition similar to those inferred for different Hermean terrains. Here we extended the study of the same materials to the VNIR region (bidirectional reflectance spectroscopy: 350 to 2500 nm). We analyzed 8 different samples with different chemical compositions, produced under different oxygen fugacity conditions We prepared eight granulometric classes between 0 and 250 μm, namely: 0-25; 25-63; 63-100; 100-125; 125-150; 150-180; 180-200 and 200-250 μm. The dominant feature in the VNIR region is due to the Fe absorption band at about 1 μm accompanied, in the more oxidized samples, by a smaller feature at 480 nm likely due to ferric oxide development. Iron free samples (FeO < 0.1 wt%) show characteristic spectral shapes with a distinctive feature at about 640 nm attributable to TiO2. Even for very low FeO content, it is possible to observe a weak yet clear band at about 900-1000 nm due to Fe absorption which explain the dominance of the spectral features due to Fe absorption at higher FeO content. Additional small bands at higher wavelengths (1300-1400 and 1900 nm) suggest a low content of water and/or –OH species in the samples. We investigated the spectral features as a function of composition, grain size and oxidation in order to gain as much information as possible on the nature of the spectra and compare them with remote sensing data or meteorites VNIR comparison. Our data on synthetic and realistic Hermean compositions will allow a better understanding of remotely acquired VisNIR spectra, which will be particularly helpful in view of the upcoming beginning of the BepiColombo ESA/JAXA mission.</p> <p> </p> <p>Acknowledgments: The authors acknowledge financial contribution from the Italian Space Agency (ASI) under ASI-INAF agreement 2017-47-H.0 (Simbio-SYS). CC, EB are also supported by agreement ASI-INAF n.2018-16-HH.0.</p>

Using Machine Learning Algorithms on Prediction of Stock Price

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.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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