Open Source Machine Learning Research Articles

Simulation for predictive maintenance using weighted training algorithms in machine learning

<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

A Personalized and Scalable Machine Learning-Based File Management System

In this work, we present a hybrid image and document filing system that we have built. When a user wants to store a file in the system, it is processed to generate tags using an appropriate open-source machine learning system. Presently, we use OpenCV and Tesseract OCR for tagging files. OpenCV recognizes objects in the images and TesserAct recognizes text in the image. An image file is processed for object recognition using OpenCV as well for text/captions process using TesserAct, which are used for tagging the file. All other files are processed using Tesseract only for generating tags. The user can also enter their own tags. A database system has been built that stores tags and the image path. Every file is stored with its owner identification and it is time-stamped. The system has a client-server architecture and can be used for storing and retrieving a large number of files. This is a highly scalable system.

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.

Medium-Term Hourly Electricity Tariff Forecasting Using Ensemble Models

Forecasting electricity tariff rates is necessary for large suppliers, consumers, and power brokers working in the wholesale markets. Meanwhile, tariff rates of the retail market are also hourly changed for certain groups of electricity consumers. It creates more efficient electrical load regulation opportunities than the traditional load leveling approach. Power facilities that include controlled load consumers or local generation can use their capabilities by adjusting the load curve according to tariff rates. This work aims to study the potential for medium-term forecasting of retail electricity tariff rates and develop a predictive machine learning model. Hourly data on the retail market tariffs of the Novosibirsk region (Siberia) for four years were collected, several machine learning models were applied, and an analysis of the input parameters for forecasting was carried out. The most significant results are the proof of the possibility of obtaining the month ahead electricity tariff rate forecast with the mean absolute percentage error 4 %. It could be used for electricity costs reduction by regulating the load curve. It was shown that the discrete models based on ensembles of logical rules give higher accuracy than models based on continuous and piecewise continuous functions, such as neural networks. The significance of the obtained results is the proposed approach for month ahead electricity tariff rates forecasting, which was verified on a four-year dataset with an error of 4 %. The approach is based on open data and open-source machine learning models, which allow specialists with even a basic level of data science skills to put it into practice.

Traffic Flow Prediction using Machine Learning Techniques - A Systematic Literature Review

Purpose: Traffic control in large cities is extremely tough. To alleviate costs associated with traffic congestion, some nations of the world have implemented Intelligent Transportation Systems (ITS). This paper reviews the application of artificial neural network (ANN) and machine learning (ML) techniques and also their implementation issues in TFP. Techniques other than ML and ANN have also been discussed. Methodology: The survey of literature on TFP (TFP) and ITS was conducted using several secondary sources of information such as conference proceedings Journals, Books, and Research Reports published in various publications, and then the kinds of literature that are reported as promising have been included. The collected information is then reviewed to discover possible key areas of concern in the TFP and ITS. Findings/Results: Traffic management in cities is important for smooth traffic flow. TFP and ITS are drawing much attention from researchers these days. Application of ML, ANN, and other techniques are being tried to alleviate the traffic flow problem in cities. TFP using ITS employing ML techniques to overcome the problem of traffic congestion looks promising. Originality: This review of literature is conducted using secondary data gathered from various sources. The information acquired will be useful to expand on existing theories and frameworks or to develop a new technique or modify to improve the accuracy of TFP. Tables containing categories of prediction, ML Pipelining, open-source ML tools available, standard datasets available have been included. Paper Type: Literature Review.

Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and devices, a longstanding culture of open-source products in other industries, and the beginnings of machine learning–friendly regulatory pathways together allow for the development and deployment of open-source machine learning models. Such tools have distinct advantages including enhanced product integrity, customizability, and lower cost, leading to increased access. However, significant questions regarding engineering concerns about implementation infrastructure and model safety, a lack of incentives from intellectual property protection, and nebulous liability rules significantly complicate the ability to develop such open-source models. Ultimately, the reconciliation of open-source machine learning and the proprietary information–driven health care environment requires that policymakers, regulators, and health care organizations actively craft a conducive market in which innovative developers will continue to both work and collaborate.

Medical Big Data Analysis Using Machine Learning Algorithms in the Field of Clinical Pharmacy

Industrial reforms utilizing artificial intelligence (AI) have been progressing remarkably around the world in recent years. In medical informatics, the application of medical big data analytics using AI is also being promoted, and it is expected to provide screening methods for predicting potential adverse drug reactions (ADRs) and discovering new effects. Previously, we developed a unique ADRs analysis system that incorporates Accord.NET, an open-source machine learning (ML) framework written in the programming language C#, and uses the Japanese Adverse Drug Event Report (JADER) database. By using this system to analyze ADRs and screening the cause and severity of ADRs, information can be obtained to evaluate efficacy as well as ADRs. Although both statistical methods and ML are commonly used for prediction, a characteristic difference between them is that the former emphasizes causal relationships and the latter emphasizes prediction results. Therefore, it is important to distinguish between cases where decisions must be made with an emphasis on causality and those where decisions must be made by focusing on unknown risks, and statistical methods and ML should be selected and used as appropriate. Against this backdrop, this paper describes a use case and suggests that the proper use of AI tools to analyze medical big data will help clinical pharmacists practice optimal drug management for each patient.

Machine Learning and the Platformization of the Military: A Study of Google's Machine Learning Platform TensorFlow

Abstract Against the background of the growing use of machine learning (ML) based technologies by the military, our article calls for an analytical perspective on ML platforms to understand how ML proliferates across the military and to what effects. Adopting a material–technical perspective on platforms as developed within new media studies, and bringing this literature to critical security studies, we suggest that a focus on platforms and the technical work they do is needed to understand how digital technologies are emerging and shaping security practices. Through a detailed study of Google's open-source ML platform TensorFlow and a discussion of the US Department of Defense Algorithmic Warfare Cross-Functional Team, or Project Maven, we make two broader contributions. First, we identify a broader “platformization” of the military, with which we refer to the growing involvement and permeation of the (technomaterial) ML platform as the infrastructure that enables new practices of decentralized and experimental algorithm development across the military. Second, we draw out how this platformization is accompanied by new entanglements between the military and actors in the corporate domain, especially Big Tech, which play a key role in this context, as well as the open-source community that is organized around these platforms.

Data Analysis and Mapping of Research Interest in Clinical Trials of Tuberculosis by Text Mining Platform of Artificial Intelligence using Open-Source Tool Orange Canvas

Background: Reading every clinical trial for any disease is tedious, as is determining the current progress, especially when the number of clinical trials is huge. The Text Mining Platform of Artificial Intelligence (AI) can help to simplify the task. Methods: A large pool of tuberculosis clinical trials has been searched through the International Clinical Trial Registry Platform (ICTRP) and used as a textual dataset. The exported dataset of 1635 clinical studies, in a comma-separated format, is preprocessed for data analysis and text mining. Data preparation, corpus generation, text preprocessing, and finally, cluster analysis were carried out using the textmining widget of the open-source machine learning tool. The hierarchical cluster analysis was used for mapping research interests in tuberculosis clinical trials. Result and Conclusion: The data mining of the exported dataset of tuberculosis clinical trials uncovered interesting facts in terms of numbers. Text mining presented a total of 41 hierarchical clusters that were further mapped in twenty-five (25) different research interests among tuberculosis clinical trials. A novel technique for the rapid and practical review of major clinical trials is demonstrated. As an open-source and GUI-based tool is used for work, any researcher with working knowledge of text mining may also use this technique for other clinical trials.

Hill Climb Game Play with Webcam Using OpenCV

Abstract: Computer vision is a process by which we can understand how the images and videos are stored and manipulated, also it helps in the process of retrieving data from either images or videos. Computer Vision is part of Artificial Intelligence. Computer-Vision plays a major role in Autonomous cars, Object detections, robotics, object tracking, etc. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. It comes with a highly improved deep learning (dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. This does allow us to take our models trained using dedicated deep learning libraries/tools and then efficiently use them directly inside our OpenCV scripts. MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions like Multi-hand Tracking, Face Detection, Object Detection and Tracking, etc.

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region

ABSTRACT This paper aims to develop a supervised classification integrating synthetic aperture radar (SAR) Sentinel-1 (S1) and optical Sentinel-2 (S2) data for land use/land cover (LULC) mapping in a heterogeneous Mediterranean forest area. The time-series of each SAR and optical bands, three optical indices (normalized difference vegetation index, NDVI; normalized burn ratio, NBR; normalized difference red-edge index, NDRE), and two SAR indices (radar vegetation index, RVI; radar forest degradation index, RFDI), constituted the dataset. The coherence information from SAR interferometry (InSAR) analysis and three optical biophysical variables (leaf area index, LAI; fraction of green vegetation cover, fCOVER; fraction of absorbed photosynthetically active radiation, fAPAR) of the single final month of the time-series were added to exploit their correlation with the canopy structure and improve the classification. The random forests (RF) algorithm was used to train and classify the final dataset, and an exhaustive grid search analysis was applied to set the optimal hyperparameters. The overall accuracy reached an F-scoreM of 90.33% and the integration of SAR improved it by 2.53% compared to that obtained using only optical data. The whole process was performed using freely available data and open-source software and libraries (SNAP, Google Earth Engine, Scikit-Learn) executed in Python-script language.

Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations

Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in the performance of central processing units (CPUs), the computational cost of simulating transient flows with extremely small time/grid scale physics is still unrealistic. In recent years, machine learning (ML) technology has received significant attention across industries, and this big wave has propagated various interests in the fluid dynamics community. Recent ML CFD studies have revealed that completely suppressing the increase in error with the increase in interval between the training and prediction times in data driven methods is unrealistic. The development of a practical CFD acceleration methodology that applies ML is a remaining issue. Therefore, the objectives of this study were developing a realistic ML strategy based on a physics-informed transfer learning and validating the accuracy and acceleration performance of this strategy using an unsteady CFD dataset. This strategy can determine the timing of transfer learning while monitoring the residuals of the governing equations in a cross coupling computation framework. Consequently, our hypothesis that continuous fluid flow time series prediction is feasible was validated, as the intermediate CFD simulations periodically not only reduce the increased residuals but also update the network parameters. Notably, the cross coupling strategy with a grid based network model does not compromise the simulation accuracy for computational acceleration. The simulation was accelerated by 1.8 times in the laminar counterflow CFD dataset condition including the parameter updating time. Open source CFD software OpenFOAM and open-source ML software TensorFlow were used in this feasibility study.

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.

Towards Skin Cancer Classification Using Machine Learning And Deep Learning Algorithms: A Comparison

Skin cancer is an uncontrolled development of abnormal skin cells potentially due to excessive exposure to sun, history of sunburns, less melanin, Precancerous skin lesions, moles, etc. This occur when unrepaired DNA damages the cells of the skin. It is one of the diseases that are viewed on its quick evolution and the most common type of cancer that endangers life. Researchers have implemented several machine learning and deep learning techniques for classification of skin cancer. In this research paper, different cancer categories are classified using significant attributes. We have used International Skin Imaging Collaboration (ISIC) dataset for classification purposes. This dermoscopic attributes dataset includes 1000 images and 10016 instances, seven categories, 5 features and 2 Meta attributes. We implemented K-Nearest Neighbor, Logistic Regression, Convolutional Neural Network, Naïve Bayes, and Decision Tree for classification and compared their performance. In order to implement classification algorithm, we used Orange which is an open-source machine learning, data mining, and data visualization toolkit. The models are evaluated based on matrices that include Accuracy, C. Automation, F1 score, Precision, Recall, and AUC. Furthermore, frequency of features is visualized using graphical method and the ROC analysis is also performed for the classifiers. It is observed that CNN technique provided the highest accuracy of 89% and the mentioned results are the highest results of classification with the state of the art techniques. For future, the improved and recent dataset and ensemble modelling techniques based on deep learning can used to enhance classification results. The research can also be extended for other cancer types using CNN.

DECODING VIEWS AND SENTIMENTS OF PROGRAM HEADS TOWARDS THE SUPERVISION OF INSTRUCTION DURING THE COVID-19 PANDEMIC

The purpose of this study was to decode the hidden views and sentiments from the collated written responses of Eastern Samar State University’s Program Heads regarding supervision of instructions amidst the COVID-19 pandemic. This study utilized Exploratory Sequential Mixed Method to explore and understand the perspective or sentiments of Eastern Samar State University program heads towards supervision of instruction in the midst of the COVID-19 pandemic. Data were collected/collated from the participants indirectly using an interview questionnaire containing an open-ended question. The same were processed and analyzed using an open-source machine learning software called Orange toolbox (Demsar et al., 2013) wherein pre-processing, sentiment analysis and topic modelling built-in tools were utilized. The results showed that the most prominent words generated by the machine learning tool from the text file of responses are the words pandemic, performance, program, learning, difficult, supervision, instruction, internet, faculty, online students, teaching, delivery confusing, challenging, poor and connectivity. The dominant sentiment associated thereof lean towards negative polarity which implicate negative sentiments. Hidden topics were automatically generated by the machine which allowed the researchers to come up with the following related themes: “Impact of pandemic in the supervision of instruction of faculty and learning of students”, “Challenges in the delivery of instruction and supervision due to poor internet connectivity”, and “Strategic role of online modalities and connectivity in supervision and delivery of instruction”. There are limited researches navigating in text mining and sentiment analysis with the use of Orange toolbox particularly those that deals with supervision of instruction in a Philippine State University. There are related studies using machine learning software, but nothing like this study directed towards a specific gap in specific locale. KEYWORDS: Pandemic, Latent Semantic Indexing, Orange Toolbox, Sentiment Analysis, Thematic Analysis.

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.

Circulating Tumor Cell Assay Enables Prediction of Recurrence Following Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: An Interpretable Machine Learning Study

Despite the effectiveness of stereotactic body radiotherapy (SBRT) in the local control of early-stage non-small cell lung cancer (ES-NSCLC), recurrence rates remain high. Such results may be improved with selection of high-risk patients for intensified treatment, e.g., early salvage radiotherapy or adjuvant systemic therapy/immunotherapy. We previously reported that a high level of circulating tumor cell (CTC) assay counts is prognostic for increased recurrence risks. In this study, based on the same patient cohort, we aimed to determine a threshold of CTC counts using the explainable boosting machine (EBM), a new interpretable machine learning model.Our dataset comprised de-identified patients (n = 92) with biopsy-proven or clinically presumed ES-NSCLC, enrolled between August 2013 and October 2018. All patients received SBRT to a median dose of 50 Gy. Peripheral blood samples for CTC assays were collected for each patient during pre-treatment, on-treatment (between the first and last fraction), and post-treatment at follow-up intervals of 1, 3, 6, 12, 18 and 24 months. EBM is a recently developed open-source machine learning model. It is an efficient implementation of the generalized additive model with pairwise interactions (GA2M), which yields high intelligibility without sacrificing accuracy. We used the explainable boosting classifier from EBM to predict recurrence risks, including any local, nodal or distant failures during the 2-year follow-up. In addition to the CTC counts, we selected the following clinicopathologic parameters known before the time of treatment as training features: gender, race, body mass index, smoker status, pack-years, age at enrollment, tumor size, stage, histology, and fractionation. The dataset was stratified by the recurrence label, and 5-fold split into 80% training set and 20% testing set.EBM was trained on each feature in a round-robin fashion via sequential gradient boosting. The model scored the pre-treatment CTC count (PreCTC) and tumor size as the most important features, with 9.7% and 9.3% of overall score. AJCC Stage and T Stage were scored lower at 3.7% each. The average area under the curve (AUC) was 0.713 ± 0.099. From the model, we extracted the 2-year recurrence risks as a function of PreCTC, with a peak found at 6.EBM correctly scored PreCTC as the most influential feature for predicting recurrence, in agreement with our previous findings; additionally, we extracted a PreCTC threshold of 6 for clinical stratification of ES-NSCLC patients. These results illustrate how EBM may be a useful innovative tool for machine-learning aided translation of clinical data sets.

Deforestation risk in the Peruvian Amazon basin

SummaryThe prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001–2019 deforestation data to develop a predictive model and to test the model’s accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

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.