Open-source Machine Learning Framework Research Articles

Tree species classification on images from airborne mobile mapping using ML.NET

ABSTRACT Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, a popular open-source machine learning framework, to develop a model for identifying tree species in images obtained from airborne mobile mapping. These high-resolution images can be used to create detailed maps of the landscape. They can also be analyzed and processed to extract information about visual features, including tree species recognition. The deep learning model was trained using ML.NET to classify two tree species based on the combination of airborne mobile mapping images. Our approach yielded impressive results, with a maximum classification accuracy of 93.9%. This demonstrates the effectiveness of combining imagery sources with deep learning tools in ML.NET for efficient and accurate tree species classification. This study highlights the potential of the ML.NET framework for automating object classification and can provide valuable insights and information for forestry management and conservation efforts. The primary objective of this research was to evaluate the effectiveness of an approach for identifying tree species through a model generated using a combination of ortho and oblique images captured by a mobile mapping system.

On Using Conversational Frameworks to Support Natural Language Interaction in Intelligent Tutoring Systems

In this paper, we analyse the potential of conversational frameworks to support the adaptation of existing tutoring systems to a natural language form of interaction. We have based our research on a pilot study, in which the open-source machine learning framework Rasa has been used to build a conversational agent that interacts with an existing Intelligent Tutoring System (ITS) called Hypergraph Based Problem Solver (HBPS). This agent has been seamlessly integrated into the ITS to replace the previously available button-based user interface and allow the user to interact with HBPS in natural language. Once appropriately trained, the conversational agent was capable of identifying the intention of a given user utterance and extracting the relevant entities related to the message content, with average weighted F1-scores of 0.965 and 0.989 for intents and entities, respectively. Methodological guidelines are provided to generate a realistic training set that enables the creation of the required Natural Language Understanding (NLU) model and also evaluate the resulting system. These guidelines can be easily exported to other ITS and contexts, to provide an enhanced interaction based on natural language processing methods.

Machine Learning Applications in Digital Humanities

This research study explores the potential of machine learning tools and techniques to organize knowledge objects pertaining to various aspects of the gender spectrum (LGBTQIA+) in order to address the low-resource features of the LGBTQIA+ knowledge domain in Indian libraries. It aims to develop a semi-automated subject indexing system using an open source machine learning framework (Annif) and deploying the Homosaurus, a domain-specific vocabulary system. It develops programmatically a comprehensive training dataset from open-access bibliographic data sources with the help of data carpentry tools and NLP services from OpenAI. The study also measures the efficiencies of the automated indexing framework and investigates the potential for widespread adoption of a REST/API call-based approach for rapid indexing of a substantial number of records related to the LGBTQIA+ domain.

AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning.

Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software: https://github.com/vanderschaarlab/AutoPrognosis.

Application of self-adapting regularization, machine learning tools and limits in Levenberg–Marquardt algorithm to solve CNLS problem

The Electrochemical Spectroscopy Impedance (EIS) data are usually analyzed by solving different Complex Nonlinear Least Squares (CNLS) problems. These problems are generally solved by using the Levenberg–Marquardt algorithm (LMA), which is a self-adaptive regularization technique that applies the first derivatives values stored in the Jacobian matrix. The LMA convergence can be amplified by the application of both the limit tactics and the exact first derivative values. These exact values can be obtained by using the automatic differentiation (AD) algorithm embedded in PyTorch, which is an open-source machine learning framework. However, the joint application of AD, self-adapting regularization, and the limits has not been reported as a part of CNLS strategy yet.Herein, we have designed and developed a new CNLS strategy that applies the limits tactics, AD, and self-adapting regularization. Also, we claim that the new strategy can be straightforwardly employed as the exact first derivative values can be obtained by EEC and AD, which simplifies developing tasks. The results in this work clearly reveal that the LMA convergence is boosted when using the exact Jacobian matrix constructed by both the limits and AD. In addition to that, the new strategy decreases the impact of the poor starting parameters. The tests in this work, conducted by analyzing ZARCs and FRACs synthetic data, show a superior performance of the newly developed CNLS strategy.

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

A methodological approach to enable natural language interaction in an Intelligent Tutoring System

In this paper, we present and evaluate the recent incorporation of a conversational agent into an Intelligent Tutoring System (ITS), using the open-source machine learning framework Rasa. Once it has been appropriately trained, this tool is capable of identifying the intention of a given text input and extracting the relevant entities related to the message content. We describe both the generation of a realistic training set in Spanish language that enables the creation of the required Natural Language Understanding (NLU) models and the evaluation of the resulting system. For the generation of the training set, we have followed a methodology that can be easily exported to other ITS. The model evaluation shows that the conversational agent can correctly identify the majority of the user intents, reporting an f1-score above 95%, and cooperate with the ITS to produce a consistent dialogue flow that makes interaction more natural.

Classification of racehorse limb radiographs using deep convolutional neural networks.

To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Radiographs (N = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validate and test (116, 40 and 42 radiographs, respectively) six deep learning architectures available as part of the open source machine learning framework PyTorch. The deep learning architectures with the best top-1 accuracy had the batch size further investigated. Top-1 accuracy of six deep learning architectures ranged from 0.737 to 0.841. Top-1 accuracy of the best deep learning architecture (ResNet-34) ranged from 0.809 to 0.878, depending on batch size. ResNet-34 (batch size = 8) achieved the highest top-1 accuracy (0.878) and the majority (91.8%) of misclassification was due to laterality error. Class activation maps indicated that joint morphology, not side markers or other non-anatomical image regions, drove the model decision. Deep convolutional neural networks can classify equine pre-import radiographs into the 48 standard views including moderate discrimination of laterality, independent of side marker presence.

Conditional Generative Adversarial Networks for modelling fuel sprays

In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs) was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. The model consists of two sub-modules: (i) an autoencoder converting the variable length droplet trajectories into fixed length, lower dimensional representations and (ii) a Wasserstein GAN that learns to mimic the latent representations of the evaporating droplets along their lifetime. The GAN module was also conditioned with the injection location and the diameters of the droplets to increase the generalizability of the whole framework. The training data was provided from highly resolved 3D, transient Eulerian–Lagrangian, large eddy simulations conducted with OpenFOAM. Neural network models were created and trained within the open source machine learning framework of PyTorch. Predictive capabilities of the proposed method was discussed with respect to spray statistics and evaporation dynamics. Results show that conditioned GAN models offer a great potential as low order model approximations with high computational efficiency. Nonetheless, the capabilities of the autoencoder module to preserve local dependencies should be improved to realize this potential. For the current case study, the custom model architecture was capable of conducting the simulation in the order of seconds after a day of training, which had taken one week on HPC with the conventional CFD approach for the same number of droplets (200,000 trajectories).

Robot intelligent grasping experimental platform combining Jetson NANO and machine vision

According to the requirements of the intelligent manufacturing experimental teaching project, this experimental platform develops a robot intelligent grasping experimental platform based on Jetson NANO and vision. The platform adopts the Jetson NANO controller based on Google open source machine learning framework TensorFlow + Keras, cooperates with the computer vision library OpenCV to develop the machine vision algorithm, and realizes the grasping and handling of materials based on a 6-DOF articulated robot, it can support open project-based experimental teaching. The experimental platform integrates the knowledge of robot control, machine vision, mechatronics and other aspects of courses, which can train students’ ability to solve complex engineering problems and stimulate students’ creative thinking. And the experimental platform has good openness. In the experimental teaching, it only needs to specify the objectives to be achieved without restricting the specific implementation methods. Students can independently design and test the algorithm program of each link in the visual positioning system through software, and deeply experience the relevant theoretical knowledge and practical methods through hands-on experiments.

Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules

Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

GEOTHERMALCLOUD: MACHINE LEARNING FOR GEOTHERMAL RESOURCE EXPLORATION

Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, play fairway analysis (PFA) studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) have vast potential. However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive, with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCloud https://github.com/SmartTensors/GeoThermalCloud.jl. The GeoThermalCloud uses a series of un-supervised, supervised, and physics-informed ML methods available in SmartTensors AI platform https://github.com/SmartTensors. Through GeoThermalCloud, we can identify hidden patterns in the geothermal field data needed to discover blind systems efficiently. Crucial geothermal signatures often overlooked in traditional PFA are extracted using the GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA (ePFA), which is informative for efficient exploration. We applied our ML methodology to various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work). The results provide valuable insights into resource types within those regions. This ML-enhanced workflow makes the GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.

Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

An open-source machine learning framework for global analyses of parton distributions

We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions.

An open source machine learning framework for efficient and transparent systematic reviews

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool to accelerate the step of screening titles and abstracts. For many tasks—including but not limited to systematic reviews and meta-analyses—the scientific literature needs to be checked systematically. Scholars and practitioners currently screen thousands of studies by hand to determine which studies to include in their review or meta-analysis. This is error prone and inefficient because of extremely imbalanced data: only a fraction of the screened studies is relevant. The future of systematic reviewing will be an interaction with machine learning algorithms to deal with the enormous increase of available text. We therefore developed an open source machine learning-aided pipeline applying active learning: ASReview. We demonstrate by means of simulation studies that active learning can yield far more efficient reviewing than manual reviewing while providing high quality. Furthermore, we describe the options of the free and open source research software and present the results from user experience tests. We invite the community to contribute to open source projects such as our own that provide measurable and reproducible improvements over current practice.

Anomalies Detection Using Isolation in Concept-Drifting Data Streams

Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency.

Open Source Machine Learning Frameworks for Industrial Internet of Things

Abstract Information and communication technology has revolutionized the industrial operations and productions. The industries irrespective of size, whether small or large, have felt the need of artificial intelligence and machine learning techniques to process the terabytes of data generated through sensors, actuators, industrial management systems, and web applications. These data have the characteristics of volume (terabyte) and variety (image, audio, video, graphics) and thus customized models and techniques are required for analysis and management. The advancement in computer hardware, processing power, storage capacity, and cloud computing have led to experimentation and implementation of machine learning models in industrial domain for resource optimization, operation management, and quality control. However, the industrial Data Analysts face the dilemma of selecting the affordable and easy to use machine learning frameworks that suite their need and expectations. The study investigates the open source machine learning frameworks, aligned with the industrial domain (processing data generated from Industrial Internet of Things), in terms of usage, programming languages, implementations, and future prospects.

A deep energy method for finite deformation hyperelasticity

We present a deep energy method for finite deformation hyperelasticitiy using deep neural networks (DNNs). The method avoids entirely a discretization such as FEM. Instead, the potential energy as a loss function of the system is directly minimized. To train the DNNs, a backpropagation dealing with the gradient loss is computed and then the minimization is performed by a standard optimizer. The learning process will yield the neural network's parameters (weights and biases). Once the network is trained, a numerical solution can be obtained much faster compared to a classical approach based on finite elements for instance. The presented approach is very simple to implement and requires only a few lines of code within the open-source machine learning framework such as Tensorflow or Pytorch. Finally, we demonstrate the performance of our DNNs based solution for several benchmark problems, which shows comparable computational efficiency such as FEM solutions.

296 Can Machine Learning Help to Predict Delays in Acute Stroke Care?

Time to revascularization is a key factor affecting the outcome of acute stroke care. We aimed to assess the utility of machine learning with a neural network for predicting the Door to Needle time (DNT) in acute stroke care. This intervention would help alert the treatment team of potential delays associated with the arrival of new patients with an acute cerebrovascular accident (CVA). Historical patient medical records of acute stroke thrombolysis available at our hospital from 2003-2018 (15 years) were used. Data from 2003-2015, consisting of 221 cases, were used in training. Data from 2016-2018 were used for validation. A 3-layered fully connected neural network was implemented using the Tensor Flow framework (An open source machine learning framework). Duration, severity by National institute of health stroke scale (NIHSS), age in years and hour of day at arrival were identified as the predictive features. A door to needle time of above 45 minutes was set as the target for training the neural network. The network after training showed a positive predictive value of 63% and sensitivity 67% on validation, with an overall accuracy of 60%, defined as the percentage of delay > 45 minutes, which was correctly predicted. With the above 4 parameters, though individually having a weak association, it was possible to develop a prediction model for DNT with 60% accuracy and 63% positive predictive value. This type of prediction models can be a potential solution to optimize and help identify errors or delays in various acute care pathways including those for acute stroke, acute myocardial infarction, and sepsis. Application of machine learning in this context of optimizing acute care based on a predictive model has not been reported in the literature so far.

Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions

BackgroundProtein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named “Simple Classifier”, and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named “OntoNorm”.ResultsWe evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology.ConclusionOur results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles.AvailabilitySimple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.