Machine Learning-based Algorithms Research Articles

Machine learning-based nutrient classification recommendation algorithm and nutrient suitability assessment questionnaire

Machine learning-based nutrient classification recommendation algorithm and nutrient suitability assessment questionnaire

Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis

BackgroundSmall multiple intracranial aneurysms (SMIAs) are known to be more prone to rupture than are single aneurysms. However, specific recommendations for patients with small MIAs are not included in the guidelines of the American Heart Association and American Stroke Association. In this study, we aimed to evaluate the feasibility of machine learning-based cluster analysis for discriminating the risk of rupture of SMIAs.MethodsThis multi-institutional cross-sectional study included 1,427 SMIAs from 660 patients. Hierarchical cluster analysis guided patient classification based on patient-level characteristics. Based on the clusters and morphological features, machine learning models were constructed and compared to screen the optimal model for discriminating aneurysm rupture.ResultsThree clusters with markedly different features were identified. Cluster 1 (n = 45) had the highest risk of subarachnoid hemorrhage (SAH) (75.6%) and was characterized by a higher prevalence of familiar IAs. Cluster 2 (n = 110) had a moderate risk of SAH (38.2%) and was characterized by the highest rate of SAH history and highest number of vascular risk factors. Cluster 3 (n = 505) had a relatively mild risk of SAH (17.6%) and was characterized by a lower prevalence of SAH history and lower number of vascular risk factors. Lasso regression analysis showed that compared with cluster 3, clusters 1 (odds ratio [OR], 7.391; 95% confidence interval [CI], 4.074–13.150) and 2 (OR, 3.014; 95% CI, 1.827–4.970) were at a higher risk of aneurysm rupture. In terms of performance, the area under the curve of the model was 0.828 (95% CI, 0.770–0.833).ConclusionsAn unsupervised machine learning-based algorithm successfully identified three distinct clusters with different SAH risk in patients with SMIAs. Based on the morphological factors and identified clusters, our proposed model has good discrimination ability for SMIA ruptures.

Adaptive estimation of quantum observables

The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.

Spatial Interpolation of Soil Temperature and Water Content in the Land-Water Interface Using Artificial Intelligence

The distributed measured data in large regions and remote locations, along with a need to estimate climatic data for point sites where no data have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, the increasing use of artificial intelligence has become a promising alternative to conventional deterministic algorithms for spatial interpolation. The present study aims to evaluate some machine learning-based algorithms against conventional strategies for interpolating soil temperature data from a region in southeast Canada with an area of 1000 km by 550 km. The radial basis function neural networks (RBFN) and the deep learning approach were used to estimate soil temperature along a railroad after the spline deterministic spatial interpolation method failed to interpolate gridded soil temperature data on the desired locations. The spline method showed weaknesses in interpolating soil temperature data in areas with sudden changes. This limitation did not improve even by increasing the spline nonlinearity. Although both radial basis function neural networks and the deep learning approach had successful performances in interpolating soil temperature data even in sharp transition areas, deep learning outperformed the former method with a normalized RMSE of 9.0% against 16.2% and an R-squared of 89.2% against 53.8%. This finding was confirmed in the same investigation on soil water content.

A machine learning-based algorithm to identify U-500R insulin candidates among adults with type 2 diabetes mellitus in US retrospective databases

Objective To develop a machine learning-based predictive algorithm to identify patients with type 2 diabetes mellitus (T2DM) who are candidates for initiation of U-500R insulin (U-500R). Methods A retrospective cohort of patients with T2DM was used from a large US administrative claims and electronic health records (EHR) database affiliated with Optum. Predictor variables derived from the data were used to identify appropriate supervised machine learning models including least absolute shrinkage and selection operator (LASSO) and extreme gradient boosted (XGBoost) methods. Predictive performance was assessed using precision-recall (PR) and receiver operating characteristic (ROC) area under the curve (AUC). The clinical interpretation of the final model was supported by fitting the final set of variables from the LASSO and XGBoost models to a traditional logistic regression model. Model choice was determined by comparing Akaike Information Criterion (AIC), residual deviances, and scaled Brier scores. Results Among 81,242 patients who met the study eligibility criteria, 577 initiated U-500R and were assigned to the positive class. Predictors of U-500R initiation included overweight/obesity, neuropathy, HbA1c ≥9% and 8%–9%, BUN 23.8 to <112 mg/dl, ALT 35.9–2056.2 U/L, no radiological chest exams, no GFR labs, and gait/mobility abnormalities. The best performing model was the LASSO model with an ROC AUC of 0.776 on the hold-out test set. Conclusion This study successfully developed and validated a machine learning-based algorithm to identify U-500R candidates among patients with T2DM. This may help health care providers and decision-makers to understand important characteristics of patients who could use U-500R therapies which in turn could support policies and guidelines for optimal patient management.

Modelling parametric uncertainty in large-scale stratigraphic simulations

We combine forward stratigraphic models with a suite of uncertainty quantification and stochastic model calibration algorithms for the characterization of sedimentary successions in large scale systems. The analysis focuses on the information value provided by a probabilistic approach in the modelling of large-scale sedimentary basins. Stratigraphic forward models (SFMs) require a large number of input parameters usually affected by uncertainty. Thus, model calibration requires considerable time both in terms of human and computational resources, an issue currently limiting the applications of SFMs. Our work tackles this issue through the combination of sensitivity analysis, model reduction techniques and machine learning-based optimization algorithms. We first employ a two-step parameter screening procedure to identify relevant parameters and their assumed probability distributions. After selecting a restricted set of important parameters these are calibrated against available information, i.e., the depth of interpreted stratigraphic surfaces. Because of the large costs associated with SFM simulations, probability distributions of model parameters and outputs are obtained through a data driven reduced complexity model. Our study demonstrates the numerical approaches by considering a portion of the Porcupine Basin, Ireland. Results of the analysis are postprocessed to assess (i) the uncertainty and practical identifiability of model parameters given a set of observations, (ii) spatial distribution of lithologies. We analyse here the occurrences of sand bodies pinching against the continental slope, these systems likely resulting from gravity driven processes in deep sea environment.

Subspace projection-based weighted echo state networks for predicting therapeutic peptides

Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machine learning-based therapeutic peptide identification algorithms do not consider the processing of noisy samples. We propose a therapeutic peptide classifier, called weighted echo state networks based on subspace projection (WESN-SP), which reduces the bias caused by high-dimensional noisy features and noisy samples. WESN-SP is trained by sparse Bayesian learning algorithm (SBL) and introduces a weight coefficient for each sample by kernel dependence maximization-based subspace projection. The experimental results show that WESN-SP has better performance than other existing methods.

Mapping the hepatic immune landscape identifies monocytic macrophages as key drivers of steatohepatitis and cholangiopathy progression.

The progression of chronic liver diseases towards liver cirrhosis is accompanied by drastic tissue changes. This study combines elaborate transcriptomic and histological methods aiming at spatially resolving the hepatic immune microenvironment in NAFLD (including NASH, primary sclerosing cholangitis, primary biliary cholangitis, and severe alcoholic hepatitis). Human liver samples were subjected to RNA-sequencing (n=225) and imaging cytometry (n=99) across 3 independent patient cohorts. Liver samples from alcoholic hepatitis and primary biliary cholangitis patients were used for comparison. Myeloid populations were further characterized in corresponding mouse models. Imaging, clinical, and phenotypical data were combined for multidimensional analysis. NAFLD/NASH and primary sclerosing cholangitis disease stages were associated with loss of parenchymal areas, increased ductular cell accumulation, and infiltration of immune cells. NASH patients predominantly exhibited myeloid cell accumulation, whereas primary sclerosing cholangitis patients additionally had pronounced lymphoid cell responses. Correlating to disease stage, both etiologies displayed intense IBA1 + CD16 low CD163 low macrophage aggregation in nonparenchymal areas, with a distinct spatial proximity to ductular cells. Mouse models revealed that disease-associated IBA1 + hepatic macrophages originated from bone marrow-derived monocytes. Using an unbiased, machine learning-based algorithm, IBA1 in combination with hepatocyte and ductular cell immunostaining-predicted advanced cirrhosis in human NASH, primary sclerosing cholangitis, and alcoholic hepatitis. Loss of hepatocytes and increased ductular reaction are tightly associated with monocyte-derived macrophage accumulation and represent the most prominent common immunological feature revealing the progression of NAFLD, primary sclerosing cholangitis, primary biliary cholangitis, and alcoholic hepatitis, suggesting IBA1 + CD163 low macrophages are key pathogenic drivers of human liver disease progression across diverse etiologies.

Interspecific comparison of gene expression profiles using machine learning

Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)-a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focused on sequence similarity, our algorithm focuses on functional similarity inferred from the comparison of gene expression profiles. We propose novel metrics for expression pattern similarity-expression score (ES)-that is suitable for species with differing morphologies. As a proof of concept, we compare detailed transcriptome maps of Arabidopsis thaliana, the model species, Zea mays (maize) and Fagopyrum esculentum (common buckwheat), which are species that represent distant clades within flowering plants. The classifier resulted in an AUC of 0.91; under the ES threshold of 0.5, the specificity was 94%, and sensitivity was 72%.

SQL injection attack detection in network flow data

SQL injections rank in the OWASP Top 3. The literature shows that analyzing network datagrams allows for detecting or preventing such attacks. Unfortunately, such detection usually implies studying all packets flowing in a computer network. Therefore, routers in charge of routing significant traffic loads usually cannot apply the solutions proposed in the literature. This work demonstrates that detecting SQL injection attacks on flow data from lightweight protocols is possible. For this purpose, we gathered two datasets collecting flow data from several SQL injection attacks on the most popular database engines. After evaluating several machine learning-based algorithms, we get a detection rate of over 97% with a false alarm rate of less than 0.07% with a Logistic Regression-based model.

Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.

Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry

This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine learning-based techniques, GB and RF are the best two models to predict energy consumption, whereas ANN shows the predictive performance comparable to the linear regression model. Nevertheless, LSTM outperforms both statistical-based and machine learning-based algorithms in predicting industrial energy consumption.

An Insight into NeuroEvolution and Genetic Algorithms for Text Classification

Natural Language Processing (NLP) systems have, over the past decade, shifted from using rule-based techniques to using machine learning-based algorithms. This has led to the development of different architectures and models for different tasks. Some of these architectures include models like the transformers, the CNN and the RNN, which have now become ubiquitous in NLP. However, designing these neural network architectures usually requires in-depth analysis and knowledge of multiple domain areas involved with the problem at hand. In our work, we evaluate an alternative solution to this problem in the domain of text classification. Here, we suggest using the Genetic Algorithm with gradient descent (GAGD) and NeuroEvolution of Augmenting Topologies (NEAT) to search for an optimal neural architecture for the Reuters-21578 and 20 Newsgroups datasets. We evaluate and compare the results of the two algorithms against the current state-of-the-art architectures and provide insight into their performance.

Development of a product return process in the context of the Circular Economy with the help of Machine Learning

The Circular Economy aims to reintroduce the value of products back into the economic cycle at the same value chain level. While the activities of the Circular Economy are already well-defined, there exists a gap in how returned products are treated by the industry. This study aims to examine how a process should be designed to handle returned products in the context of the Circular Economy. To achieve this, a machine learning-based algorithm is used to classify data and extract relevant information throughout the product life cycle. The focus of this research is limited to land transportation systems within the Sharing Economy sector.

Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians.

Towards a Machine Learning-based Model for Automated Crop Type Mapping

In the field of smart farming, automated crop type mapping is a challenging task to guarantee fast and automatic management of the agricultural sector. With the emergence of advanced technologies such as artificial intelligence and geospatial technologies, new concepts were developed to provide realistic solutions to precision agriculture. The present study aims to present a machine learning-based model for automated crop-type mapping with high accuracy. The proposed model is based on the use of both optical and radar satellite images for the classification of crop types with machine learning-based algorithms. Random Forest and Support Vector Machine, were employed to classify the time series of vegetation indices. Several indices extracted from both optical and radar data were calculated. Harmonical modelization was also applied to optical indices, and decomposed into harmonic terms to calculate the fitted values of the time series. The proposed model was implemented using the geospatial processing services of Google Earth Engine and tested with a case study with about 147 satellite images. The results show the annual variability of crops and allowed performing classifications and crop type mapping with accuracy that exceeds the performances of the other existing models.

High Impedance Fault Location Methods: Review and Harmonic Selection-Based Analysis

High Impedance Faults (HIFs) are recurring events in electrical Distribution Systems (DSs) and occur by the contact between energized conductors and high impedance surfaces. HIFs may pose hazards to living beings and cause bushfires. However, the HIF protection has not been completely solved due to the small fault current and varying impedance, inhibiting traditional protection techniques from functioning correctly. In the literature, researchers have mainly focused on detection techniques. Thus, the development of HIF Location Methods (HIFLMs) is recent, and evidences for conclusive solutions are still lacking. Moreover, to this date, no existing study reviews the main challenges concerning HIFLMs in DSs. This paper proposes a systematic analysis of the common stages to design the main existing HIFLMs. The strategy is evaluating the similar characteristics that pose a common research path regarding challenges faced in real-world conditions. Additionally, this paper proposes a case study to assess the best input signals, metrics, and machine learning-based decision algorithms of a new HIFLM. The results are promising, with high identification rates, even in noisy conditions. The methodology can help to select the datasets for supervised learning-based HIFLM. Highlighting the state-of-art of current methods and support development of HIFLMs are this paper’s main contributions.

Progress in the Application of Mathematical Algorithms in Materials Science

As materials are the direct drive for scientific development and social progress, the research on their structure and properties has always been a research hotspot in materials science. At present, the discovery of new materials is mainly carried out by the researcher's intuitive judgment of materials and a large number of "trial and error" experiments, which is inefficient and difficult to effectively discover a large number of possible new material combinations. Materials science has long relied on two major means of experiment and calculation. The machine learning-based mathematical algorithms offers a new research method. This article systematically expounds the common mathematical algorithms and the basic processes of their calculation methods, details the application of mathematical algorithms in material performance prediction and new material design, and gives a summarization and outlook of the challenges and application prospects concerning their application in materials science.

Machine Learning-Based Inversion Algorithm for Particle Size Distribution of Non-Spherical Particle System

Machine Learning-Based Inversion Algorithm for Particle Size Distribution of Non-Spherical Particle System

Brain-inspired Artificial Intelligence and Machine Learning-based Navigation Algorithms, Spatial Data Visualization and Digital Twin Modeling tools, and Emotion Detection and Recognition Technologies in the Decentralized and Interconnected Metaverse

Brain-inspired Artificial Intelligence and Machine Learning-based Navigation Algorithms, Spatial Data Visualization and Digital Twin Modeling tools, and Emotion Detection and Recognition Technologies in the Decentralized and Interconnected Metaverse