Intelligent Machine Learning Algorithms Research Articles

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

BackgroundThe many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important.ObjectivesTo assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease.DesignThis is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups.SummaryThis trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines.Trial registrationClinicalTrials.Gov Identifier: NCT05377320

Slope Stability Prediction Method Based on Intelligent Optimization and Machine Learning Algorithms

Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable state of the slope as the prediction object and used the unit weight, cohesion, internal friction angle, pore water pressure coefficient, slope angle, and slope height as prediction indices to analyze the slope stability based on the collection of 117 slope data points. The genetic algorithm was used to solve the hyperparameters of machine learning algorithms by simulating the phenomena of reproduction, hybridization, and mutation in the natural selection and natural genetic processes. Five algorithms were used, including the support vector machine, random forest, nearest neighbor, decision tree, and gradient boosting machine models. Finally, all of the obtained stability prediction results were compared. The prediction outcomes were analyzed using the confusion matrix, receiver characteristic operator (ROC), and area under the curve (AUC) value. The AUC values of all machine learning prediction results were between 0.824 and 0.964, showing excellent performance. Considering the AUC value, accuracy, and other factors, the random forest algorithm with KS cutoff was determined to be the optimal model, and the relative importance of the influencing variables was studied. The results show that cohesion was the factor that most affects slope stability, and the influence factor was 0.327. This study proves the effectiveness of the integrated techniques for slope stability prediction, makes essential suggestions for future slope stability analysis, and may be extensively applied in other industrial projects.

Cooperative adaptive management of the Nile River with climate and socio-economic uncertainties

The uncertainties around the hydrological and socio-economic implications of climate change pose a challenge for Nile River system management, especially with rapidly rising demands for river-system-related services and political tensions between the riparian countries. Cooperative adaptive management of the Nile can help alleviate some of these stressors and tensions. Here we present a planning framework for adaptive management of the Nile infrastructure system, combining climate projections; hydrological, river system and economy-wide simulators; and artificial intelligence multi-objective design and machine learning algorithms. We demonstrate the utility of the framework by designing a cooperative adaptive management policy for the Grand Ethiopian Renaissance Dam that balances the transboundary economic and biophysical interests of Ethiopia, Sudan and Egypt. This shows that if the three countries compromise cooperatively and adaptively in managing the dam, the national-level economic and resilience benefits are substantial, especially under climate projections with the most extreme streamflow changes.

Ethical Challenges in Open Learning Analytics

Open learning analytics (OLA) is a new research field focusing to create an open platform for integration of heterogeneous learning environments. The central notion in OLA is openness, and it relates to architectures, processes, access, and datasets. Learning analytics (LA) usually employs artificial intelligence (AI) technologies and machine learning (ML) algorithms to provide information and predictions, thus enhancing learning experience. AI-based systems provide many benefits, but their implementation raises ethical concerns due to wide range of possible negative consequences. OLA approach amplifies ethical concerns related to AI-based systems, such as privacy, transparency, bias, fairness, etc. In this paper we discuss a comprehensive list of ethical issues in OLA along with mitigations procedures.

Classification Predictive Maintenance Using XGboost with Genetic Algorithm

This study develops a condition classification system of compressor 103J and water pump systems which are key equipment in the ammonia production line, hence the monitoring of these two very important machines. In recent years, there are many good intelligent machine learning algorithms and XGboost is one of them. However, it contains many parameters and classification performance of the model will be greatly affected by the selection of parameters and their combination technique. In this paper, XGboost algorithm is combined with the genetic algorithm, called GA-XGboost, in order to find the best hyper parameters of classifiers which makes the classifier more efficient and ensures the proper functioning of compressor 103J and water pump systems. Experiments show that GA-XGboost algorithm has improved the accuracy of classification in the compressor 103J and the water pump dataset compared with other machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF) and AdaBoost. Also experiments demonstrate the improvement of the GA-XGboost algorithm by the combination of different selection and crossover operators of the genetic algorithm.

Short-Term Stock Price Prediction Algorithm Construction Based on Integrated Learning of SVR and RF with Bagging

In the context of the emergence of artificial intelligence machine learning algorithms, how to handle training data for reasonable and accurate prediction of the stock future market is expected to bring effective methods, but previous traditional models are difficult to be used effectively in complex stock markets. In this paper, by exploring new stock forecasting methods such as Support Vector Regression (SVR), Random Forest (RF), and integration based on integrated learning, the model results are compared with previous traditional model results and the models are evaluated using R2 and MSE metrics. The algorithm based on Bagging integration has better robustness and generalization, in which both R2 and MSE have some improvement compared with those before integration. The research in this paper is beneficial to provide a reasonable prediction approach for stock forecasting later, which can help consumers make better quantitative trading.

Abstract 9796: Identification of an Optimal Blood Pressure by Images of Correction of the Pathological Mechanism by Hypertension: An Angiographic, Machine Learning Analysis and Application for Personalized Medicine

Introduction: As hypertension (HTN) is a strong risk factor of coronary artery disease (CAD), what is an optimal blood pressure (BP) which could preclude CAD? Hypothesis: In industrial piping, laminar flow preserves equipment service life while turbulent flow damages the inner surface of pipes and pumps. Based on the same hydraulic principles, our hypothesis is built on the observations that turbulent flow injures the intima and triggers atherosclerosis. In order to identify an optimal BP, at which BP level, could we evidence VISUALLY the abolishment of turbulent flow at the iliac artery as proof of correction of the injuring mechanism precipitated by HTN? Methods: 210 patients with uncontrolled HTN who underwent coronary intervention and a repeat diagnostic angiogram a few months later because of unstable angina were enrolled. At the end each coronary procedure, patients underwent iliac angiogram for deployment of closure device. In a new technique of dynamic angiography, the contrast was injected until the iliac artery was completely opacified. As the injection stopped, the blood flew down and displaced the contrast. The movements of the blood in white color above a black background could be clearly identified and analyzed. At the same time, Artificial Intelligence (Machine Learning algorithms) program had 2 models built on Python. Model 1 was based on U-net and Densenet-121 for vessel segmentation. Model 2 was used for classification and movement of flow. Model 2 was trained based on the convolutional neural network. The main measurements were the type of flow (turbulent), directions (antegrade versus retrograde) and presence of collision from slamming of the retrograde against antegrade flow. Results: At baseline, 98% patients had prolonged reversed flow with turbulence. At the 2nd iliac angiogram, if the systolic BP was <110mmHg, the rate of turbulence was 10%. If the systolic BP >130mmHg, the turbulence was present in 70% (p<0.05) Conclusions: With the uncontrolled BP imaged as collision at the iliac artery secondary to heightened distal vascular resistance, the abolishment of turbulence and return of laminar flow confirmed that the ideal systolic BP was <110mmH in the application of personalized medicine. Larger studies need to confirm the above result.

Modelling and prediction of binder content using latest intelligent machine learning algorithms in carbon fiber reinforced asphalt concrete

In the present work, an attempt is made to find the most suitable prediction model for Marshall Stability and the optimistic Bitumen Content (BC) in carbon fiber reinforced asphalt concrete for flexible pavements by performing Marshall Stability tests. Further the prediction analysis is performed by taking the cognizance of the published research articles. Twofold approaches are adopted; first, to find the most suitable model to predict the Marshall Stability and second to obtain the optimum binder content responsible for the highest strength. Further, to find the most suitable model for closer prediction of Marshall Stability, eighteen input parameters i.e., Binder Content (BC with fifteen variations); 4.20%, 4.30%, 4.50%, 4.90%, 5.00%, 5.10%, 5.15%, 5.20%, 5.23%, 5.30%, 5.34%, 5.40%, 5.50%, 6.00%, 6.50%, and three others i.e., Carbon fiber, Bitumen grade and Fiber length are applied in the modelling algorithm. Five Machine learning techniques viz., Support Vector Machine, Gaussian Process, Random Forest, Random Tree, and M5P model were employed to find the most suitable prediction model. Seven statistical metrices i.e., Coefficient of correlation (CC), Mean absolute error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE), Willmott's index (WI), and Nash- Sutcliffe coefficient (NSE) were used to evaluate the performance of the applied models. After performing modelling analysis, it has been found that the Random Forest-based model is outperforming amongst all applied models with CC as 0.9735, MAE as 1.1755, RMSE as 1.5046, RAE as 25.68%, RRSE as 26.93%, WI values as 0.9351, and NSE values as 0.9272 in the testing stage. The Taylor diagram of the testing dataset also conforms to the results of RF-based model. The sensitivity analysis demonstrates that binder content (BC) of about 5.0% has a significant influence on the Marshall Stability in the asphalt mix used with carbon fibers.

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R2, RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques

The COVID-19 pandemic is one of the most disruptive outbreaks of the 21st century considering its impacts on our freedoms and social lifestyle. Several methods have been used to monitor and diagnose this virus, which includes the use of RT-PCR test and chest CT/CXR scans. Recent studies have employed various crowdsourced sound data types such as coughing, breathing, sneezing, etc., for the detection of COVID-19. However, the application of artificial intelligence methods and machine learning algorithms on these sound datasets still suffer some limitations such as the poor performance of the test results due to increase of misclassified data, limited datasets resulting in the overfitting of deep learning methods, the high computational cost of some augmentation models, and varying quality feature-extracted images resulting in poor reliability. We propose a simple yet effective deep learning model, called DeepShufNet, for COVID-19 detection. A data augmentation method based on the color transformation and noise addition was used for generating synthetic image datasets from sound data. The efficiencies of the synthetic dataset were evaluated using two feature extraction approaches, namely Mel spectrogram and GFCC. The performance of the proposed DeepShufNet model was evaluated using a deep breathing COSWARA dataset, which shows improved performance with a lower misclassification rate of the minority class. The proposed model achieved an accuracy, precision, recall, specificity, and f-score of 90.1%, 77.1%, 62.7%, 95.98%, and 69.1%, respectively, for positive COVID-19 detection using the Mel COCOA-2 augmented training datasets. The proposed model showed an improved performance compared to some of the state-of-the-art-methods.

Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms.

Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine’s processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality.

Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review.

Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (n = 39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (n = 1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region

The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR), random forest (RF), and multivariate adaptive regression splines (MARS); novel ensemble approaches i.e. MLP-Bagging, KLR-Bagging, RF-Bagging and MARS-Bagging in the Kurseong-Himalayan region. For the ensemble models the RF, KLR, MLP and MARS were used as base classifiers, and Bagging was used as meta classifier. Another objective of the current work is to introduce and evaluate the effectiveness of the novel KLR-Bagging and MARS-Bagging ensembles in susceptibility to landslide. Compiling 303 landslide locations to calibrate and test the models, an inventory map was created. Eighteen LCFs were chosen using the Relief-F and multi-collinearity tests for mapping the landslide susceptibility. Applying receiver operating characteristic (ROC), precision, accuracy, incorrectly categorized proportion, mean-absolute-error (MAE), and root-mean-square-error (RMSE), the LSMs were subsequently verified. The different validation results showed RF-Bagging (AUC training 88.69% & testing 92.28%) with ensemble Meta classifier gives better performance than the MLP, KLR, RF, MARS, MLP-Bagging, KLR-Bagging, and MARS-Bagging based LSMs. RF model showed that the slope, altitude, rainfall, and geomorphology played the most vital role in landslide occurrence comparing the other LCFs. These results will help to reduce the losses caused by the landslides in the Kurseong and in other areas where geo-environmental and geological conditions more or less similar.

Artificial intelligence algorithms for the recognition of Brugada type 1 pattern on standard 12-leads ECG

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This research project is funded by Tuscany Region Background/Introduction Electrocardiograms (ECGs) are rapidly moving from analog to digital versions. Consequently, a series of automatic analyses of standard 12-lead ECGs are attracting interest for their ability to support clinicians in the automatic recognition of specific features associated with different cardiac diseases [2]. Artificial Intelligence applications and Machine Learning (ML) algorithms have gained much attention in the last years for their ability to figure out patterns from data independently, without being explicitly taught rules. Peculiar features define the ECGs of patients with Brugada Syndrome (BrS); however, ambiguities still exist for the correct diagnosis of BrS and discrimination with respect to other pathologies. Purpose The BrAID (Brugada syndrome and Artificial Intelligence applications to Diagnosis) project aims to develop an innovative system for diagnosing Type 1 BrS based on ECG pattern recognition through the application of ML algorithms. In this work, an application of Echo State Networks (ESN), a type of Recurrent Neural Network (RNN), for the diagnosis of BrS from ECG is presented. Methods After approval from the Local Ethical Committees, 12-lead ECGs were obtained in patients enrolled in 5 Centers diagnosed with typical spontaneous Type 1 pattern (coved) (group A, 81 patients). Baseline ECG was also collected in patients undergoing the ajmaline test, classified as positive (group B, 37 patients) or negative (group C, 14 patients) according to test results. 174 patients with no clinical and familial history of arrhythmias were considered controls (group D). Data were collected from 4 beats extracted from the ECGs as input to the ESN. The datasets obtained in the different groups were used for the ESN model’s training and assessment (testing) through a double cross-validation approach. Results As shown in Table 1, the performances using three leads (V1, V2, V3) or V2 only were compared. The algorithm performance was assessed in all the datasets (group A+B+C+D) and in spontaneous BrS (group A) and controls (group D). A good accuracy (79.21%) was seen when the three leads were considered for groups A and D only; the best test set accuracy (80.20%) was obtained in the case in which V2 only was used as input in all the datasets. Conclusion(s) In this work, a novel system for diagnosing Type 1 BrS using an ESN approach was developed. Our preliminary results show that this ML model is able to detect ECG patterns associated with Type 1 BrS with good and comparable accuracy both when three leads (79.21% ) or V2 only (80.20%) were analyzed. The future availability of larger datasets could improve the model performance, increasing the ESN potentialities as a clinical support system tool to be used in everyday clinical practice. Table 1. The accuracy, specificity, and sensitivity reported for each dataset group are obtained through double cross-validation.

Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.

Direct tire slip ratio estimation using intelligent tire system and machine learning algorithms

Accurate estimation of the tire slip ratio is critical for vehicle safety, as it is necessary for vehicle control purposes. In this paper, an intelligent tire system is presented to develop a novel slip ratio estimation model using machine learning (ML) algorithms. The accelerations, generated by a triaxial accelerometer installed onto the inner liner of the tire, are varied when the tire rotates to update the contact patch. Meanwhile, the reference value of slip ratio can be measured by the MTS Flat-Trac tire test platform. Then, by analyzing the variation between the accelerations and slip ratio, highly useful features are discovered, which are especially promising for assessing vertical acceleration. For these features, ML algorithms are trained to build the slip ratio estimation model, in which the ML algorithms include artificial neural networks (ANNs), gradient boosting machines (GBMs), random forests (RFs), and support vector machines (SVMs). Finally, the estimated NRMS errors are evaluated using 10-fold cross-validation (CV). The proposed estimation model is able to estimate the slip ratio continuously and stably using only the acceleration from the intelligent tire system, and the estimated slip ratio range can reach 30%. The estimation results have high robustness to vehicle velocity and load, where the best NRMS errors can reach 4.88%. In summary, the present study with the fusion of an intelligent tire system and machine learning paves the way for the accurate estimation of the tire slip ratio under different driving conditions, which creates new opportunities for autonomous vehicles, intelligent tires, and tire slip ratio estimation.

The Construction of Corporate Financial Management Risk Model Based on XGBoost Algorithm

Corporate financial management is a tedious task, and it is a complicated thing to rely solely on the human resources of financial personnel to manage. With the continuous development of intelligent algorithms and machine learning algorithms, new ideas have been brought to enterprise financial risk assessment. This method will not only save a lot of financial and material resources but also improve the accuracy of enterprise financial risk assessment. Compared with machine learning algorithms such as random forests and support vector machines, the extreme gradient boosting (XGBoost) algorithm is more widely used, and it has unique advantages in terms of speed and accuracy. This study selects the XGBoost learning algorithm to predict the risk assessment in corporate finance. In this study, the data preprocessing method is used to preprocess and classify the enterprise financial data source effectively, and then the XGBoost algorithm is used to assess the risk of enterprise financial data, and finally a set of enterprise financial risk assessment model is established. The research results show that the XGBoost model selected in this paper has high reliability in predicting the financial risk assessment of enterprises, and the prediction errors are all within 3%. The largest forecast error is only 2.68%, which comes from the profit and loss of the enterprise’s financial situation. The smallest error is only 0.56%, which is a trustworthy enough error for corporate financial forecasting. There is a high correlation between the type of enterprise financial risk assessment and the actual type of risk. At the same time, this paper also has a good dependence on the preprocessing method of enterprise financial data.

Research themes in machine learning applications in supply chain management using bibliometric analysis tools

PurposeThis paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.Design/methodology/approachUnlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.FindingsUsing contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.Originality/valueModern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis.

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and to improve routine medical and public health practice. Understanding patients' multi-omics make-up in conjunction with the clinical data will lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and to optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. Precision medicine promotes integrating collective and individualized clinical data with patient-specific multi-omics data to develop therapeutic strategies and knowledge bases for predictive and personalized medicine in diverse populations. Artificial intelligence approaches and machine learning algorithms will add additional capabilities to precision medicine that will leverage and extend the information contained within the original data and facilitate modeling patient-specific multi-omics data against publicly available annotation data for better understanding disease mechanisms. This chapter discusses emerging, significant, and recently reported multi-omics, deep phenotyping, and translational approaches to facilitate the implementation of precision medicine, as well as innovative, smart, and robust big-data platforms that are necessary to improve the quality and transition of healthcare by analyzing heterogeneous healthcare and multi-omics data.