Contribution Of Machine Learning Research Articles

Solutions of Navier Stokes equation and application on aerodynamics

The Navier-Stocks equation (NSE) was derived based on Newtons second law and Eulers equation with the viscosity effect. The continuity of mass, conservation of momentum and energy contribute to the motion of fluid. This paper discusses the hypothesis and theories of the solution of the 3D NSE corresponding to the boundary and initial conditions from previous research. Meanwhile, this paper focuses on the study of solutions and turbulence models of NSE contributed to the applications of aerodynamics. Machine Learning and Neural Networks are applied to the solution of the NSE to improve the accuracy of prediction of fluid motion. Aerodynamics applications on airfoil, turbulence model, design of propeller and ejection seats are discussed with analysis of solutions of Navier-Stocks equation. With the contribution of Machine learning, accurate and global solutions are expected to be computed for the NSE in the future.

Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Abstract This article reports on the state-of-the-art and future perspectives of machine learning (ML) in petrology. To achieve this goal, it first introduces the basics of ML, including definitions, core concepts, and applications. Then, it starts reviewing the state-of-the-art of ML in petrology. Established applications mainly concern the so-called data-driven discovery and involve specific tasks like clustering, dimensionality reduction, classification, and regression. Among them, clustering and dimensionality reduction have been demonstrated to be valuable for decoding the chemical record stored in igneous and metamorphic phases and to enhance data visualization, respectively. Classification and regression tasks find applications, for example, in petrotectonic discrimination and geo-thermobarometry, respectively. The main core of the manuscript consists of depicting emerging trends and the future directions of ML in petrological investigations. I propose a future scenario where ML methods will progressively integrate and support established petrological methods in automating time-consuming and repetitive tasks, improving current models, and boosting discovery. In this framework, promising applications include (1) the acquisition of new multimodal petrologic data; (2) the development of data fusion techniques, physics-informed ML models, and ML-supported numerical simulations; and (3) the continuous exploration of the ML potential in petrology. To boost the contribution of ML in petrology, our main challenges are: (1) to improve the ability of ML models to capture the complexity of petrologic processes, (2) progressively link ML algorithms with the physical and thermodynamic nature of the investigated problems, and (3) to start a collaborative effort among researchers coming from different disciplines, both in research and teaching.

Artificial Intelligence to Improve Antibiotic Prescribing: A Systematic Review.

Introduction: The use of antibiotics leads to antibiotic resistance (ABR). Different methods have been used to predict and control ABR. In recent years, artificial intelligence (AI) has been explored to improve antibiotic (AB) prescribing, and thereby control and reduce ABR. This review explores whether the use of AI can improve antibiotic prescribing for human patients. Methods: Observational studies that use AI to improve antibiotic prescribing were retrieved for this review. There were no restrictions on the time, setting or language. References of the included studies were checked for additional eligible studies. Two independent authors screened the studies for inclusion and assessed the risk of bias of the included studies using the National Institute of Health (NIH) Quality Assessment Tool for observational cohort studies. Results: Out of 3692 records, fifteen studies were eligible for full-text screening. Five studies were included in this review, and a narrative synthesis was carried out to assess their findings. All of the studies used supervised machine learning (ML) models as a subfield of AI, such as logistic regression, random forest, gradient boosting decision trees, support vector machines and K-nearest neighbours. Each study showed a positive contribution of ML in improving antibiotic prescribing, either by reducing antibiotic prescriptions or predicting inappropriate prescriptions. However, none of the studies reported the engagement of AB prescribers in developing their ML models, nor their feedback on the user-friendliness and reliability of the models in different healthcare settings. Conclusion: The use of ML methods may improve antibiotic prescribing in both primary and secondary settings. None of the studies evaluated the implementation process of their models in clinical practices. Prospero Registration: (CRD42022329049).

Artificial intelligence, language and Neuropsychological measures in Alzheimer’s disease

AbstractBackgroundOne of the current challenges in the field of Alzheimer’s disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low‐cost features originating from the language and neuropsychological assessment.MethodWe aim at performing a meta‐analytic evaluation of the contribution of ML and language/neuropsychological measures for the automated classification of AD and MCI patients and the prediction of MCIs’ conversion to AD‐type dementia. Specifically, we study, from the results reported by independent studies, whether ML algorithms, trained on a set of language and neuropsychological measures, could be used for the automatic diagnosis of MCI and AD‐type dementia. To tackle the issue of cross‐linguistic sensitivity, this work spans cohorts of speakers from widely different languages. The advantages and issues that should be taken under consideration for translating these approaches into reliable clinical studies are discussed.ResultAlthough a high heterogeneity was observed, this meta‐analysis shows that ML applied to language and neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of language and neuropsychological tests can be extracted by ML that maximize the classification accuracy.ConclusionML applied on language and neuropsychological measures can automatically classify AD patients, even at an early stage of the disease. This brings several advantages, such as the development of more objective and efficient language and neuropsychological batteries for improving the early diagnosis of AD. Future studies should empirically test the combination of methodological features necessary to improve patients’ classification also at the preclinical stages.

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

Machine Learning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.

Contribution of machine learning in continuous improvement processes

PurposeThe aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.Design/methodology/approachThis work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.FindingsThe three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.Originality/valueAs the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO2 trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.

Role of machine and organizational structure in science.

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.

Prediction analysis on the pre and post COVID outbreak assessment using machine learning and deep learning

<span>In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.</span>

Face Mask Detection Using Machine Learning

Abstract: COVID-19 pandemic has rapidly affected ourday-to-day life disrupting the world trade and movements. Wearing a protective face mask hasbecome a new normal. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. Therefore, face maskdetection has become a crucial task to help global society. This paper presents a simplified approach to achieve this purpose using some basic Machine Learning packages like TensorFlow, Keras and OpenCV. The application of “machine learning” and “artificial intelligence” has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents.The proposed method detects the face from the image correctly and then identifies if it has a mask on it or not. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the mobileNetV2 which is a Convolutional Neural Network architecture to detect the presence of masks correctly without causing over- fitting.

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

Integrating machine learning, modularity and supply chain integration for Branding 4.0

While brands use technologies in various ways to improve their performance, they appear to struggle with achieving Branding 4.0 standards. This new generation of brand development has brought an era of hyper-customized experiences to benefit brand performance. With the Branding 4.0 literature still in its infancy, questions remain regarding how brands can maintain their identity while delivering a hyper-personalized customer experience. This study draws on mass customization, artificial intelligence, and supply chain management literature to investigate how three core organizational capabilities and resources—machine learning, modularity, and supply chain integration—helpful in achieving production flexibility could jointly enable companies to transition to and maintain a Branding 4.0 philosophy through more efficient personalization of their product offerings. This paper reports findings from 15 in-depth interviews with top executives from brands, including some Fortune Global 500 companies, in China's garment and footwear industries to provide insights into Branding 4.0 and the possible contribution of machine learning, modularity application, and supply chain integration. Our findings inform a two-tier response strategy and a three-dimensional analytical framework which provide a theoretical basis for operationalizing Branding 4.0 and exploring, through a resource orchestration lens, how brands can respond to the related adoption challenges. Specifically, our findings show how machine learning's data analysis, knowledge conversion, and transmission capabilities could benefit both modular management and supply chain tasks to optimize product co-design processes and timely responses to customers' changing demands.

Feature importance: Opening a soil-transmitted helminth machine learning model via SHAP.

In the field of landscape epidemiology, the contribution of machine learning (ML) to modeling of epidemiological risk scenarios presents itself as a good alternative. This study aims to break with the ”black box” paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health, using the prevalence of hookworms, intestinal parasites, in Ethiopia, where they are widely distributed; the country bears the third-highest burden of hookworm in Sub-Saharan Africa. XGBoost software was used, a very popular ML model, to fit and analyze the data. The Python SHAP library was used to understand the importance in the trained model, of the variables for predictions. The description of the contribution of these variables on a particular prediction was obtained, using different types of plot methods. The results show that the ML models are superior to the classical statistical models; not only demonstrating similar results but also explaining, by using the SHAP package, the influence and interactions between the variables in the generated models. This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

A Brief Overview on the Contribution of Machine Learning in Systems Neuroscience

Background: The use of Machine Learning (ML) is witnessing exponential growth in the field of artificial intelligence (AI) and neuroscience, in particular in subdisciplines such as Systems Neuroscience (SN), as a viable alternative to the use of classical statistical techniques. The combination of this interconnection allows a more detailed study of algorithms and neural circuits that emulate core cognitive processes. ML toolbox includes algorithms that are suited to solving problems of classification, regression, clustering, and anomaly detection. Objective: The aim of the present opinion was to exemplify the contribution of ML in the field of SN in three different fields: 1) cognitive modelling; 2) neuroimaging; 3) analysis of clinical datasets. Method: We gathered evidence from the relevant literature related to the interaction between neuroscience and AI and the impact of ML in SN. Results : ML is specifically suited to the analysis of large clinical neuroscience datasets. Experimental results in neuroscience are hard to replicate for a number of reasons, and ML may contribute to attenuating these replicability issues via the ubiquitous use of cross-validation procedures. While ML modelling is primarily focused on prediction accuracy, one of the drawbacks in ML is the opacity of various algorithms that resist intuitive understanding. Conclusions: Future avenues of research have already been traced and include increased interpretability of currently opaque ML models functioning and causal analysis. Causal analysis is intended to distinguish between spurious associations and cause-effect relationships and is a primary interest in both clinical medicine and basic neuroscience.

Value proposition operationalization in peer-to-peer platforms using machine learning

The purpose of this paper is to operationalize the value proposition in peer-to-peer platforms, by analyzing from all the variables which ones contribute the most for being an Airbnb Superhost. Authors use two different Machine Learning methods: Boruta for feature selection and SVM classification for prediction. More than 250 variables from 5136 listings were analyzed in the Canary Islands region. Results indicate that the Peer-to-Peer Platform Value proposition can be decomposed into three components: shared resources, value package and communications. Value proposition operationalization shows the possibilities and contribution of Machine Learning in the field of Tourism and Marketing. As practical implications for hosts, relevant variables help to have an understanding of the potential not addressed in their own value proposition. For Airbnb, relevant variables could be highlighted in search results or filters. For other companies, relevant variables of the value proposition can help to operationalize. • Value proposition for peer-to-peer accommodation platforms share value elements. • Superhost represent the best value proposition configuration. • 58 variables were found relevant to be a Superhost with AUC of 84,87%. • Communication and value package components contain most relevant variables.

What kind of novelties can machine learning possibly generate? The case of genomics

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming potential bias by exploring more systematically the space of possible hypotheses implied by a theory.

The relationship between cognition and vocational training outcome in patients with acquired brain injury: Contribution of Machine Learning

The aim of this study was to identify specific cognitive patterns related to long-term vocational training outcome. Records of twenty-eight patients who had benefited from a professional rehabilitation program were retrospectively processed. Screening through machine learning algorithms of patients’ neuropsychological scores identified cognitive patterns related to both vocational training outcomes: succeeded or failed. These patterns were based on cognitive performance intervals even if the cognitive ability was not impaired. The cognitive pattern related to a successful vocational training included performance intervals on measures underlying verbal memory consolidation, visual memory incidental recall, problem solving and planning abilities. The cognitive pattern explaining failure of vocational training included performance intervals on tasks involving planning and problem solving abilities. From a comprehensive neuropsychological battery, memory and executive measures appeared to be the best attributes related to the vocational training outcome in patients with brain injury. Even with a cognitive functioning above the pathological cutoff, patients suffering from a brain injury could fail a vocational training. The attributes related to the vocational training outcome would be more a specific level of cognitive functioning rather than an interpretation of neuropsychological scores only based on a normal versus pathological distinction.

Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.