Supervised Machine Learning Methods Research Articles

LightWarner: Predicting Failure of 3D NAND Flash Memory Using Reinforcement Learning

NAND flash memory has gained popularity in a wide variety of digital storage systems. Although with excellent performance, NAND flash memory suffers various reliability problems. In recent years, researchers try to predict flash failure by using machine-learning models. However, the application of machine-learning based failure prediction method faces the following problems: imbalance between robustness and portability. When applying on different flash chips, the performance of prediction model degrades with the variation of error characteristics. In order to adapt to the variation, the machine-learning model needs to be re-built to ensure performance of failure prediction. The overheads of re-building model result in challenges when adjusting prediction model to adapt to the variation of error characteristics. To overcome these challenges, we present LightWarner, an easily applicable predictor based on model-free Reinforcement learning algorithms. LightWarner learns error characteristics dynamically during flash lifetime without pre-training. We evaluate the performance of LightWarner on six types of 3D flash chips. The evaluation result shows that LightWarner achieves over 93% F1 score on different flash chips, which is about 10% higher than supervised machine learning methods. And LightWarner can adapt to the variation of error characteristics with low migration costs.

Applying Existing Datasets as a Pseudo Corpus for Sentiment Representation on Social Media

This paper proposes a method to represent the sentiment characteristics of opinions on social media by using some datasets as a pseudo corpus without any annotations. The widespread social media enables us to easily share our own opinions on the Web and communicate with each other. The more the data on social media increase, the more the demands for analysis of the data increase, e.g., text classification and sentiment analysis. Usually, the annotated data should be required in the existing text classification using the supervised machine learning method. However, it is reasonable to say that the criteria for the annotated labels should differ for each period, culture, and independent sense. Effective text classification for such different criteria needs the different types of annotations corresponding to each measure, and it requires much time and human resources. A pseudo corpus consists of multiple existing datasets with different characteristics in the proposed method. The classification model for each dataset is obtained as learning the pseudo corpus. The sentiment of the input text, which domain is different from the learned datasets, is represented as the likelihood distribution for the datasets in the pseudo corpus. This paper discusses the potential and limitations of this idea through the experiment.

Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction

The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.

Selection a group of features based on machine learning algorithms to simplify psycho-technical examination

In this paper, we propose to apply supervised machine learning methods to simplify the psychological assessment of professional drivers. The simplification comes from reducing the number of psychological tests used for drivers’ examinations in particular situations. We propose to perform recursive test elimination, and we describe the comparison of applying the support vector machine and random forest classifiers to discover the optimal psychological test combination.

CFD modeling and optimal design of SiC deposition on the fuel combustion nozzle in a commercial CVD reactor

This work presents a new CFD model developed to investigate the SiC film growth over a fuel combustion nozzle in the commercial hot-wall CVD reactor. A detailed 3D model consisting of the compressive transport models and the reaction mechanism to account for the chemistry of the gas-phase and surface reactions are developed. The velocity, temperature, and concentration profiles inside the reactor are predicted numerically. The multispecies transport and its interplay with the gas and surface reactions are understood to operate the reactor effectively. The natural convection and hydrodynamics stability of the flow is investigated using various dimensionless groups (Re, Pr. Pe, and Gr/Re2). It has been observed that the buoyancy-driven flow is dominant at a large Reynolds number (Re) and Gr/Re2 ratio. This results in flow recirculation, which ultimately deteriorates the film uniformity. A sensitivity analysis is performed to identify how critical parameters affect the deposition process. Besides, the optimisation of the CVD reactor is also served by combining the support vector machine, a versatile supervised machine learning method, and the Nelder-Mead algorithm to improve the film quality. Our results demonstrate that the present methodology effectively obtains optimal process conditions, significantly enhancing the film performance.

Customer experience discovery model based on sentiment analysis and machine learning method

Exploiting customer insights plays a key role in the long-term development strategy of every business, which strongly supports them in building trust with customers and enhancing competitive advantages. Especially in the period of rapid growth of technology, as there is a significant increase in online users, all the interactions and comments become more important in extracting valuable insights into customer behaviours and emotions. Our research aims to conduct sentiment analysis on comments in mobile commerce apps , then classify them into positive or negative sentiment, based on a hybrid approach that combines supervised machine learning methods and natural language processing techniques; at the same time, evaluating the model performance through the Confusion Matrix to ensure the reliability of the results. The study experimented on a proposed model with 04 machine learning methods including Naive Bayes, SVM, Random Forest and Logistic Regression on more than 935,000 comments collected from 04 popular mobile commerce apps in Vietnam (Tiki, Shopee, Lazada and Sendo). The experiment categorised positive and negative views with high accuracies of 91%, expressed by reports and charts that reflect customer trends and feelings. Moreover, the study also brings new and deeper perspectives on customer behaviours, assisting administrators to detect the strengths and weaknesses of services and apps, thereby improving user experience. Based on the research results, E-commerce businesses can analyse market trends and explore customers' needs and interests to develop effective product and service development strategies.

Quay Contact Detection for Ships using Motion Sensors and Machine Learning

Detecting when and where contact with the quay appears is an important ability for ships undergoing a docking maneuver. The motion of the quay, environmental forces, and hydrodynamic effects caused by the interaction between the ship and the quay are all important factors that affect if a docking procedure succeeds or not. This paper studies how contact with the quay can be detected using conventional motion sensors. This includes sensor data from inertial measurement units, global navigation satellite systems, and estimates from an inertial navigation system. The contact detection task can be interpreted as a binary classification problem deciding if contact has occurred or not. Three conventional supervised machine learning methods are studied using experimental data captured in full-scale experiments. Logistic regression, a support vector machine and a long-term short memory network have been trained and investigated. The results are promising and a proof of concept illustrating that supervised machine learning is a viable strategy for quay contact detection using motion sensors.

Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

Expendable to essential? Changing perceptions of gig workers on Twitter in the onset of COVID-19

ABSTRACT While ride-hailing ridership declined in 2020 due to COVID-19 induced restrictions like stay-at-home orders, food/grocery delivery services became quasi-essential. This study investigates if and how public perceptions of gig work related to platform-based ride-hailing and food/grocery delivery services changed during the early stages of the pandemic. We collected a sample of 23,845 Twitter posts (‘tweets’) related to these platform-based services within two-week periods before and after the US COVID-19 emergency declaration. Sentiment analysis on tweets was conducted to investigate changes in public perception of gig work. Tweet content was analyzed by descriptively coding about 10% of the sample of tweets manually along ten different dimensions (e.g., personal experience, informative, and about driver); then we used thematic analysis to gain an understanding about the public’s views towards gig work/workers. We tested supervised machine learning methods to explore their potential to classify the rest of the sample along the ten descriptive dimensions. The number of tweets increased by approximately 150% after the emergency declaration and became more positive in sentiment. Qualitative results indicate that tweets about negative personal experiences with drivers/companies decreased during COVID-19, while tweets exhibiting a sense of community (e.g., sharing information) and concern towards gig workers increased. Findings can inform policy and workforce changes regarding platform-based service companies.

Multiple-Timescale Neural Networks: Generation of History-Dependent Sequences and Inference Through Autonomous Bifurcations.

Sequential transitions between metastable states are ubiquitously observed in the neural system and underlying various cognitive functions such as perception and decision making. Although a number of studies with asymmetric Hebbian connectivity have investigated how such sequences are generated, the focused sequences are simple Markov ones. On the other hand, fine recurrent neural networks trained with supervised machine learning methods can generate complex non-Markov sequences, but these sequences are vulnerable against perturbations and such learning methods are biologically implausible. How stable and complex sequences are generated in the neural system still remains unclear. We have developed a neural network with fast and slow dynamics, which are inspired by the hierarchy of timescales on neural activities in the cortex. The slow dynamics store the history of inputs and outputs and affect the fast dynamics depending on the stored history. We show that the learning rule that requires only local information can form the network generating the complex and robust sequences in the fast dynamics. The slow dynamics work as bifurcation parameters for the fast one, wherein they stabilize the next pattern of the sequence before the current pattern is destabilized depending on the previous patterns. This co-existence period leads to the stable transition between the current and the next pattern in the non-Markov sequence. We further find that timescale balance is critical to the co-existence period. Our study provides a novel mechanism generating robust complex sequences with multiple timescales. Considering the multiple timescales are widely observed, the mechanism advances our understanding of temporal processing in the neural system.

Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction.

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF.Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771–0.895) and validation set (AUC 0.798, 95% CI: 0.719–0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05).Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

A Survey of Recent Indoor Localization Scenarios and Methodologies.

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Using Unsupervised and Supervised Machine Learning Methods to Correct Offset Anomalies in the GOES‐16 Magnetometer Data

AbstractThis study uses supervised and unsupervised machine learning (ML) methods to correct unwanted offsets observed in the NOAA GOES‐16 magnetometer data. All GOES satellites have an inboard and outboard magnetometer sensor mounted along a long boom. Post‐launch testing of the GOES‐16 magnetometers found that the inboard sensor suffers significant thermally induced magnetic contamination and currently only the outboard sensor is used in NOAA operations. The contamination varies both diurnally and seasonally making it very difficult to correct using basic statistical methods. For simplicity in explaining the offsets we are trying to correct, and methods used, we focus on correcting only one of the inboard vector components, the E‐component (Earthward). We start by applying the unsupervised k‐Shape method to the magnetic field vector E‐component outboard minus inboard sensor time series, ΔE, resulting in four clusters that are closely related to the time of year and the solar β angle, which is a measure of the amount of time that a satellite is in direct sunlight. We then utilized LSTM networks as regressors to correct the offsets observed in GOES‐16 inboard sensor E‐component data. We trained our LSTMs using GOES‐17 magnetometer data, which we show to exhibit much less variability compared with the GOES‐16 data. The correction results reduced the offsets in the clusters from between 3–5 nT and 0–2 nT standard deviations. The combining of unsupervised and supervised ML methods is a powerful technique that can be applied to space‐based instruments that produce time series data.

Supervised machine learning method tailored for five brain MRI events is predictive of cognitive status

AbstractBackgroundDespite the large neuroanatomic measurements obtained from brain MRI segmentation, we do not have yet a definitive neuroanatomic or vascular feature/even that can precisely delineate a cognitively normal subject from cognitively impaired and Alzheimer’s disease (AD). Increasing lifespan of population accompanied with increased prevalence of cardiovascular risks across globe demands for precise early detection of AD and cognitive status (impaired or normal) in the aging individuals. Here, we have employed machine learning (ML) method on neuroanatomic volume and white matter hyperintensity (WMH) volume obtained from MRI analyses of data from National Alzheimer’s Coordinating Center (NACC) to identify participants as cognitively normal (CN), cognitively impaired with etiological diagnosis as AD and cognitively impaired (CI) subjects.MethodBrain segmentation outputs for neuroanatomic‐volume and White matter hyperintensity (WMH) was obtained from NACC for 1290 unique subjects with total of 3684 longitudinal‐MRI. Supervised ML was performed using the MRI parameters for identifying the subjects as AD, CI, and CN. 50% of the MRI were used as training set while another 50% was used as test. Various combinations of neuroanatomic volume with WMH quantity were tested for accuracy of delineating the AD, CI and CN groups using random forest method. Furthermore, various ML methods such as XGB classifiers, classification tree, bagging classifier and simple classification were tested for accuracy using the optimized neuroanatomic volume and WMH.ResultRandom forest method converged with a combination of 5‐MRI features, intracranial, gray‐matter, cerebral CSF, hippocampus, and White‐matter hyperintensity volume with highest accuracy (83.3%) in identifying AD, CI, and CN subjects (Fig. 1). Inclusion of normal appearing white‐matter volume during the ML optimization of MRI features led to significant reduction in the accuracy. Simple random forest ML method showed highest accuracy using the optimized MRI features (Fig. 2).ConclusionAll the neuroanatomic events obtained from brain MRI may not be specific in providing diagnostic features for evaluating brain health. Here we show that a combination of 5 MRI features that includes microvascular pathology and neuroanatomic volume may provide a specific and efficient non‐invasive imaging biomarker to evaluate brain health in aging population.

Identifying wildlife observations on twitter

Despite the potential of social media for environmental monitoring, concerns remain about the quality and reliability of the information automatically extracted. Notably there are many observations of wildlife on Twitter, but their automated detection is a challenge due to the frequent use of wildlife related words in messages that have no connection with wildlife observation. We investigate whether and what type of supervised machine learning methods can be used to create a fully automated text classification model to identify genuine wildlife observations on Twitter, irrespective of species type or whether Tweets are geo-tagged. We perform experiments with various techniques for building feature vectors that serve as input to the classifiers, and consider how they affect classification performance. We compare three classification approaches and perform an analysis of the types of features that are indicative for genuine wildlife observations on Twitter. In particular, we compare some classical machine learning algorithms, widely used in ecology studies, with state-of-the-art neural network models. Results showed that the neural network-based model Bidirectional Encoder Representations from Transformers (BERT) outperformed the classical methods. Notably this was the case for a relatively small training corpus, consisting of less than 3000 instances. This reflects that fact that the BERT classifier uses a transfer learning approach that benefits from prior learning on a very much larger collection of generic text. BERT performed particularly well even for Tweets that employed specialised language relating to wildlife observations. The analysis of possible indicative features for wildlife Tweets revealed interesting trends in the usage of hashtags that are unrelated to official citizen science campaigns. The findings from this study facilitate more accurate identification of wildlife-related data on social media which can in turn be used for enriching citizen science data collections.

EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality

BackgroundTo select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment.ResultsEvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study.ConclusionsEvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.

Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs

Forecasting productivity of shale gas has gained much attention in recent years. However, few researchers considered forecasting absolute open flow potential (AOFP). The conventional method of determining the AOFP of gas wells is through a systematic well testing which is time consuming and has adverse economic effects. Therefore, the objective of this research is to establish an efficient and applicable method for forecasting shale gas well AOFP. In this study, we combined one of the most classic unsupervised machine learning methods namely, principal component analysis (PCA) with least squares support vector regression (LSSVR) which is an efficient supervised machine learning method in forecasting the AOFP for shale gas wells. This method was named hybrid machine learning method based on PCA and LSSVR model (LSSVR-PCA). The proposed method was applied to the Weiyuan, Changning as well as Zhaotong shale gas reservoirs in the Sichuan basin, southwest China. Results showed that the proposed method could forecast AOFP with high accuracy and efficiency if adequate well data are accessible. This hybrid machine learning method also provides an intelligent approach for field engineers to forecast the AOFP and appropriately allocate production of gas wells.

Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.