scholarly journals Automated detection of grapes and leaves in viticulture with a YOLOv2 neural network

2018 ◽  
Author(s):  
Andreza Aparecida dos Santos ◽  
Sandra Eliza Fontes de Avila ◽  
Thiago Teixeira dos Santos
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Task ◽  
Automated Detection ◽  
Supervised Machine Learning ◽  
Convolutional Network ◽  
Grape Varieties

In this work, we modeled the problem of detection of fruit and leaves in viticulture for proximal applications as a supervised machine learning task. We created and manually labeled a database of images obtained at Guaspari Winery. In total, the database consists of 11.883 images of bunch of grapes and leaves. We trained a convolutional network with YOLOv2 architecture to locate and classify bunch of grapes and leaves. Quantitative tests have shown results for detection and classification with precision of 100%, recall of 74,22% and F1-Score up to 85,2% for the class “grape”. Also, qualitative tests show that the model generalizes well when tested on photographs of other grape varieties. These results are promising and are moving towards the possibility of application in the field.

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

scholarly journals Testing swampland conjectures with machine learning

2020 ◽  
Vol 80 (8) ◽  
Author(s):  
Nana Cabo Bizet ◽  
Cesar Damian ◽  
Oscar Loaiza-Brito ◽  
Damián Kaloni Mayorga Peña ◽  
J. A. Montañez-Barrera
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Vacuum Energy ◽  
Matrix Theory ◽  
Scalar Potential ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
De Sitter ◽  
Learning Techniques ◽  
Isotropic Torus

Abstract We consider Type IIB compactifications on an isotropic torus $$T^6$$T6 threaded by geometric and non geometric fluxes. For this particular setup we apply supervised machine learning techniques, namely an artificial neural network coupled to a genetic algorithm, in order to obtain more than sixty thousand flux configurations yielding to a scalar potential with at least one critical point. We observe that both stable AdS vacua with large moduli masses and small vacuum energy as well as unstable dS vacua with small tachyonic mass and large energy are absent, in accordance to the refined de Sitter conjecture. Moreover, by considering a hierarchy among fluxes, we observe that perturbative solutions with small values for the vacuum energy and moduli masses are favored, as well as scenarios in which the lightest modulus mass is much smaller than the corresponding AdS vacuum scale. Finally we apply some results on random matrix theory to conclude that the most probable mass spectrum derived from this string setup is that satisfying the Refined de Sitter and AdS scale conjectures.

scholarly journals Comparison of Word Embeddings for Extraction from Medical Records

2019 ◽  
Vol 16 (22) ◽  
pp. 4360
Author(s):  
Aleksei Dudchenko ◽  
Georgy Kopanitsa
Keyword(s):  
Neural Network ◽  
Medical Records ◽  
Data Extraction ◽  
Russian Language ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Network Architectures ◽  
Medical Texts ◽  
Convolutional Network ◽  
Personalized Health

This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763).

scholarly journals Identification of Natural Selection in Genomic Data with Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to order to make inferences on demographic and adaptive processes using genomic data, Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Experiments performed on simulated data show that the proposed model can accurately predict neutral and selection processes on genomic data with more than 99% accuracy.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

scholarly journals Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3492 ◽  
Author(s):  
Jongkwon Choi ◽  
Youngmin Choo ◽  
Keunhwa Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Density Matrix ◽  
Spectral Density ◽  
Input Data ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Matrix Elements ◽  
Spectral Density Matrix ◽  
Cross Spectral Density

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

scholarly journals Identification of natural selection in genomic data with deep convolutional neural network

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Arnaud Nguembang Fadja ◽  
Fabrizio Riguzzi ◽  
Giorgio Bertorelle ◽  
Emiliano Trucchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Natural Selection ◽  
Convolutional Neural Network ◽  
Simulated Data ◽  
Genomic Data ◽  
Relevant Information ◽  
Supervised Machine Learning ◽  
Selection Processes ◽  
Adaptive Processes

Abstract Background With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Training performed on simulated data show that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy.

scholarly journals Bionic arm: Mapping of elbow and wrist flexion using neural network and fuzzy logic

2021 ◽  
Vol 9 (4B) ◽  
Author(s):  
Aiswarya Lakshmi M ◽  
◽  
Anjan Kumar Dash ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fuzzy Logic ◽  
Supervised Machine Learning ◽  
Limb Amputation ◽  
Learning Models ◽  
Wrist Flexion ◽  
Joint Angles ◽  
Joint Flexion ◽  
Machine Learning Models

Cases on limb amputation necessitate the use of Transhumeral bionic for artificial limb rehabilitation, which is controlled using Electromyographic (EMG) signals from the muscles. Before the implementation of EMG control, a mapping between the movements of an arm to the angle formed at the corresponding joints is essential to be made. Most of the works in the field of Bionics use Supervised Machine Learning models, chiefly Classification, to map muscle flexion signals to joint actuations in the bionic arm. Ample literature is also there, which uses fuzzy logic for mapping. However, there are very few literatures that compare these two methods of mapping. In this article, 2 models have been discussed regarding the mapping, and their effectiveness is compared. The first model captures elbow and wrist flexion and maps them to their respective angular displacements of joints using a fuzzy logic model. In the second model, a Pattern Recognition Artificial Neural Network (ANN) model under Supervised Machine Learning is incorporated to map elbow and wrist flexion to the corresponding joint angular displacement. The ANN is trained with elbow and wrist joint flexion values and its corresponding joint angles data, optimized, and tested in real-time. This model is verified by comparing the joint angles of a test person (measured using Goniometers) with the joint angles of Bionic models made (using a 360° protractor sheet). The second model gave the insight that supervised machine learning models provide an accurate mapping to the joint flexion in the field of bionics.

scholarly journals Machine learning model development for predicting road transport GHG emissions in Canada

2019 ◽  
Vol 53 (2) ◽  
pp. 55-72
Author(s):  
Mohd Jawad Ur Rehman Khan ◽  
Anjali Awasthi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Ghg Emissions ◽  
Predictive Performance ◽  
Road Transport ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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