Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Hemachandran, K; Tayal, Shubham; George, Preetha Mary; Singla, Parveen; Köse, Utku

doi:10.1201/9781003164265

Cited by 9 publications

(4 citation statements)

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“…Production environment (Figure 3e): aim to implement the validated functionalities in a Petrobras proprietary software named GeoqView, making the system available to endusers offering explainability and human autonomy. In the developer environment (Figure 3a), a complete ML processing chain is implemented for building two predictive models GMex and GoM, comprising the following steps: (i) data pre-processing, including the normal scores transformation; (ii) exploratory data analysis; (iii) feature selection; (iv) training and testing employing Artificial Neural Network (ANN) [65], Random Forest (RF) [66], Gaussian Naive Bayes (GNB) [67], Linear Discriminant Analysis (LDA) [68], Support Vector Machine (SVM) [69], Logistic Regression (LR) [70], and K Nearest Neighbour (KNN) [71] algorithms; and (v) assessment and selection of the better-developed models, saving the learned predictive functions for future applications. Moreover, there is an extensive scientific literature explaining each one of the ML algorithms employed in this research [70,72,73], their use in remote sensing [74,75], as well as specific examples of their application for oil slicks detection using SAR data [22,30,32,[34][35][36][37][38][39][41][42][43][76][77][78][79][80][81][82][83][84].…”

Section: Methodology For Predictive Models Development Application An...mentioning

confidence: 99%

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

et al. 2023

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The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (DS) follow the same statistical distribution of unknown samples to be predicted in the target domain (DT). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the DS and DT domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the DS and DT domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution.

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Section: Methodology For Predictive Models Development Application An...mentioning

confidence: 99%

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

et al. 2023

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“…The other models are at a considerable distance behind in terms of performance (+0.8 points in accuracy). Gaussian Naive Bayes (Gaussian NB) is a probabilistic classifier that applies Bayes' theorem with the assumption of naive independence between features [46]. It is assumed that the values associated with each feature are distributed according to a Gaussian distribution [47].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Ginovart-Panisello,

Iriondo,

Panisello Monjo

et al. 2024

Applied Sciences

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Acoustic studies on poultry show that chicken vocalizations can be a real-time indicator of the health conditions of the birds and can improve animal welfare and farm management. In this study, hens vaccinated against infectious laryngotracheitis (ILT) were acoustically recorded for 3 days before vaccine administration (pre-reaction period) and also from vaccination onwards, with the first 5 days being identified as the “reaction period” and the 5 following days as “post reaction”. The raw audio was pre-processed to isolate hen calls and the 13 Mel-frequency cepstral coefficients; then, the spectral centroid and the number of vocalizations were extracted to build the acoustic dataset. The experiment was carried out on the same farm but in two different houses. The hens from one house were assigned to the control group, without administration of the anti-inflammatory product, and the other formed the treatment group. Both acoustic data sets were recorded and processed in the same way. The control group was used to acoustically model the animal reaction to the vaccine and we automatically detected the hens’ vaccine reactions and side effects through acoustics. From Scikit-Learn algorithms, Gaussian Naive Bayes was the best performing model, with a balanced accuracy of 80% for modeling the reactions and non-reactions caused by ILT in the control group. Furthermore, the importance of algorithm permutation highlighted that the centroid and MFCC4 were the most important features in acoustically detecting the ILT vaccine reaction. The fitted Gaussian Naive Bayes model allowed us to evaluate the treatment group to determine if the vocalizations after vaccine administration were detected as non-reactions, due to the anti-inflammatory product’s effectiveness. Of the sample, 99% of vocalizations were classified as non-reactions, due to the anti-inflammatory properties of the product, which reduced vaccine reactions and side effects. The non-invasive detection of hens’ responses to vaccination to prevent respiratory problems in hens described in this paper is an innovative method of measuring and detecting avian welfare.

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“…Machine learning is a branch of AI, which explores ways to make computers improve efficiency based on experience 3 . Machine learning can enable robots to memorize long-term routes, and quickly plan the shortest path based on future use, and will follow the user's Use records to push related endpoints.…”

Section: Machine Learningmentioning

confidence: 99%

Autonomous Navigation Building Climbing And Handing Robot Based on SLAM

Chen

Peng

2022

Proceedings of International Conference on Artificial Life and Robotics

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When there is no elevator at the station and airport or the elevator is crowded, the ramp of baggage checking is steep, the weight of luggage package is too heavy, it takes time and effort, and it is very easy to knock against and cause damage to the contents of the package.Our climbing robot carries out autonomous navigation based on visual SLAM. Equipped with ROS robot operating system, it adopts laser SLAM mapping navigation technology and tracks as running parts, which is suitable for various terrains. The adoption of binocular vision, can be more accurate analysis of the surrounding environment. The robot is equipped with MPU9250 attitude sensor, which is convenient for solving robot attitude. Jetson Nano was used as the upper computer and STM32F429 as the lower computer.

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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Cited by 9 publications

References 0 publications

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy

Autonomous Navigation Building Climbing And Handing Robot Based on SLAM

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