Supervised Machine Learning Techniques Research Articles

Identifying Determinants of Dyslexia: An Ultimate Attempt Using Machine Learning

Research based on traditional linear techniques has yet not been able to clearly identify the role of cognitive skills in reading problems, presumably because the process of reading and the factors that are associated with reading reside within a system of multiple interacting and moderating factors that cannot be captured within traditional statistical models. If cognitive skills are indeed indicative of reading problems, the relatively new nonlinear techniques of machine learning should make better predictions. The aim of the present study was to investigate whether cognitive factors play any role in reading skill, questioning (1) the extent to what cognitive skills are indicative of present reading level, and (2) the extent to what cognitive skills are indicative of future reading progress. In three studies with varying groups of participants (average school-aged and poor readers), the results of four supervised machine learning techniques were compared to the traditional General Linear Models technique. Results of all models appeared to be comparable, producing poor to acceptable results, which are however inadequate for making a thorough prediction of reading development. Assumably, cognitive skills are not predictive of reading problems, although they do correlate with one another. This insight has consequences for scientific theories of reading development, as well as for the prevention and remediation of reading difficulties.

Signal classification with machine learning

This paper investigates and evaluates several Machine Learning techniques for the proper identification and classification of analytical signals. Signals having different “shapes” and periods were defined analytically to have pre-determined class associations. Supervised Machine Learning techniques were then investigated to evaluate the Machine Learning methodology’s ability to properly classify the analytical signals based on characteristics of interest.

Early prediction of SARS-CoV-2 reproductive number from environmental, atmospheric and mobility data: A supervised machine learning approach

IntroductionSARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results. Material and methodsOur primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms. ResultsData from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights. DiscussionThe model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery.

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Trust Prediction: Use of Decision Tree in Training & Evaluation

The growth of Cloud Computing is the outcome of numerous benefits associated with this technology. Like: Reduce Energy consumption, waste & carbon emission and affluence & Comfort of its working apparatus. At the same point of time its security concern is making vibrations in the research Community and regulating organizations to find the means of making it a secure and reliable stream where consumers’ interests are ensured & protected. More uncertainty arises in this sector, when data theft cases are cracked up and consumers have compromised some confidential details due to intentional or unintentional action of Cloud providers. It leads the need of Trust evaluation of the provider. This trust evaluation should be in the quantitative terms and completed before selection & registration with the provider for cloud services. This paper is using predictive modeling technique to predict the trust level of the provider. Classification, a supervised Machine Learning technique that uses certification attainment status of the provider to predict its trust level.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

ObjectiveWe sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality...

Estimating the density of deep eutectic solvents applying supervised machine learning techniques

Deep eutectic solvents (DES) are recently synthesized to cover limitations of conventional solvents. These green solvents have wide ranges of potential usages in real-life applications. Precise measuring or accurate estimating thermophysical properties of DESs is a prerequisite for their successful applications. Density is likely the most crucial affecting characteristic on the solvation ability of DESs. This study utilizes seven machine learning techniques to estimate the density of 149 deep eutectic solvents. The density is anticipated as a function of temperature, critical pressure and temperature, and acentric factor. The LSSVR (least-squares support vector regression) presents the highest accuracy among 1530 constructed intelligent estimators. The LSSVR predicts 1239 densities with the mean absolute percentage error (MAPE) of 0.26% and R2 = 0.99798. Comparing the LSSVR and four empirical correlations revealed that the earlier possesses the highest accuracy level. The prediction accuracy of the LSSVR (i.e., MAPE = 0. 26%) is 74.5% better than the best-obtained results by the empirical correlations (i.e., MAPE = 1.02%).

An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research

An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research

Cyber Attacks Prediction using Data Science

Cyber-attack, via cyberspace, targeting an enterprise's use of cyberspace for the purpose of disrupting, disabling, destroying, or maliciously controlling a computing environment/infrastructure; or destroying the integrity of the data or stealing controlled information. The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyber-attack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify type of network attack. In this paper, we model cyber-attack prediction as a classification problem, Networking sectors have to predict the type of Network attack from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni- variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 06 Issue: 03 | March - 2022 ISSN: 2582-3930 © 2022, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM11906 | Page 2 in predicting the type cyber Attacks. We classify four types of attacks are DOS Attack, R2L Attack, U2R Attack, Probe attack. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity and Entropy. Keywords Cyber-attack, Cyberspace, DOS Attack, R2L Attack, U2R Attack, Probe attack

An across online social networks profile building approach: Application to suicidal ideation detection

With the spreading use of online social networks (OSNs) such as Facebook, Twitter and Youtube, most people find that writing about their feelings and sharing their preferences and thoughts in social media is actually easier then articulating them in real life. Specifically, these networks are increasingly associated with different social phenomena such as diseases, depression and even suicide. In this context, most people who have suicidal ideations and are active in OSNs give signals of their intentions. It is therefore essential to increase efforts working on the detection of profiles whose owners have suicidal ideations as an attempt to prevent suicide. For that purpose, in this paper we propose a new method that automatically detects suicidal users through their created profiles in OSNs. Our contribution consists in considering profiles from multiple data-sources and detecting suicidal users based on their available shared content across OSNs. We extract several types of features from the posting content of users to build a complete profile that contribute to high suicidal user prediction. We employ supervised machine learning techniques to distinguish between suicidal and non-suicidal profiles. Our experiments on a dataset, which consists of persons who had died by suicide, demonstrate the feasibility of identifying user profiles from multiple data-sources in revealing suicidal profiles.

Crowdsmelling: A preliminary study on using collective knowledge in code smells detection

Code smells are seen as major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. We proposed the crowdsmelling approach based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue. This paper presents the results of a validation experiment for the crowdsmelling approach. In the context of three consecutive years of a Software Engineering course, a total "crowd" of around a hundred teams, with an average of three members each, classified the presence of 3 code smells (Long Method, God Class, and Feature Envy) in Java source code. These classifications were the basis of the oracles used for training six machine learning algorithms. Over one hundred models were generated and evaluated to determine which machine learning algorithms had the best performance in detecting each of the aforementioned code smells. Good performances were obtained for God Class detection (ROC=0.896 for Naive Bayes) and Long Method detection (ROC=0.870 for AdaBoostM1), but much lower for Feature Envy (ROC=0.570 for Random Forrest). Obtained results suggest that crowdsmelling is a feasible approach for the detection of code smells, but further validation experiments are required to cover more code smells and to increase external validity.

Lithology prediction from well log data using machine learning techniques: A case study from Talcher coalfield, Eastern India

Coal exploration in the Indian scenario is challenging due to plenty of carbon contents and dirt bands within a coal seam. Manual interpretation of geophysical logging data in such conditions is time-consuming and tedious due to the non-linear behaviors of well log signals. This paper intends to apply supervised machine learning techniques such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting technique (XGBoost) for meticulously interpreting such banded coal seams from geophysical logs. To investigate the efficacy of the above-mentioned five ML techniques in the Indian scenario, we have considered Gamma-ray, Density, and Resistivity logs from four boreholes drilled in Talcher coalfield, Eastern India. ML model training results indicate that all the prediction models have scored more than 88% accuracy scores in classifying carbonaceous and non-coal lithofacies. Receiver Operating Characteristics (ROC) curves of different ML models suggest that the obtained area under the curve (AUC) is positive and above the main diagonal for all lithotypes. Finalized ML models with appropriate hyper-parameters are also applied on the nearby drilled wells, and the outputs are validated against geological core data. It is found that all ML models have acquired an overall accuracy score greater than 80% in all three test wells, conveying that ML techniques are a potential solution for dealing with banded coal seam problems in the Indian scenario.

Machine Learning on Soccer Player Positions

During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. In this paper, we propose an approach aimed to recognize the player position in a soccer match, predicting the specific zone in which the player is located in a specific moment. Similar objectives have never been considered yet with our best knowledge. We consider supervised machine learning techniques by considering a dataset obtained through video capturing and tracking system. The data analyzed refer to several professional soccer games captured at the Alfheim Stadium in Tromso, Norway. The approach can be used in real-time, in order to verify if a player is playing according to the guidelines of the coach. In the experimental analysis, three different types of classification have been performed, i.e., three different divisions of the field, reaching the best results with Random Tree Algorithm.

Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors.

Leukotrienes (LTs) are pro-inflammatory lipid mediators derived from arachidonic acid (AA), and their high production has been reported in multiple allergic, autoimmune, and cardiovascular disorders. The biological synthesis of leukotrienes is instigated by transfer of AA to 5-lipoxygenase (5-LO) via the 5-lipoxygenase-activating protein (FLAP). Suppression of FLAP can inhibit LT production at the earliest level, providing relief to patients requiring anti-leukotriene therapy. Over the last 3 decades, several FLAP modulators have been synthesized and pharmacologically tested, but none of them could be able to reach the market. Therefore, it is highly desirable to unveil the structural requirement of FLAP modulators. Here, in this study, supervised machine learning techniques and molecular modeling strategies are adapted to vaticinate the important 2D and 3D anti-inflammatory properties of structurally diverse FLAP inhibitors, respectively. For this purpose, multiple machine learning classification models have been developed to reveal the most relevant 2D features. Furthermore, to probe the 3D molecular basis of interaction of diverse anti-inflammatory compounds with FLAP, molecular docking studies were executed. By using the most probable binding poses from docking studies, the GRIND model was developed, which indicated the positive contribution of four hydrophobic, two hydrogen bond acceptor, and two shape-based features at certain distances from each other towards the inhibitory potency of FLAP modulators. Collectively, this study sheds light on important two-dimensional and three-dimensional structural requirements of FLAP modulators that can potentially guide the development of more potent chemotypes for the treatment of inflammatory disorders.

A survey on machine learning methods for churn prediction

The diversity and specificities of today’s businesses have leveraged a wide range of prediction techniques. In particular, churn prediction is a major economic concern for many companies. The purpose of this study is to draw general guidelines from a benchmark of supervised machine learning techniques in association with widely used data sampling approaches on publicly available datasets in the context of churn prediction. Choosing a priori the most appropriate sampling method as well as the most suitable classification model is not trivial, as it strongly depends on the data intrinsic characteristics. In this paper, we study the behavior of eleven supervised and semi-supervised learning methods and seven sampling approaches on sixteen diverse and publicly available churn-like datasets. Our evaluations, reported in terms of the Area Under the Curve (AUC) metric, explore the influence of sampling approaches and data characteristics on the performance of the studied learning methods. Besides, we propose Nemenyi test and Correspondence Analysis as means of comparison and visualization of the association between classification algorithms, sampling methods and datasets. Most importantly, our experiments lead to a practical recommendation for a prediction pipeline based on an ensemble approach. Our proposal can be successfully applied to a wide range of churn-like datasets.

Defect structure classification of neutron-irradiated graphite using supervised machine learning

Molecular dynamics simulations were performed to predict the behavior of graphite atoms under neutron irradiation using large-scale atomic/molecular massively parallel simulator (LAMMPS) package with adaptive intermolecular reactive empirical bond order (AIREBOM) potential. Defect structures of graphite were compared with results from previous studies by means of density functional theory (DFT) calculations. The quantitative relation between primary knock-on atom (PKA) energy and irradiation damage on graphite was calculated.and the effect of PKA direction on the amount of defects is estimated by counting displaced atoms. Defects are classified into four groups: structural defects, energy defects, vacancies, and near-defect structures, where a structural defect is further subdivided into six types by decision tree method which is one of the supervised machine learning techniques.

Using machine learning to identify factors associated with practice location of the healthcare workforce.

Past studies examined factors associated with rural practice, but none employed newer machine learning (ML) methods to explore potential predictors. The primary aim of this study was to identify factors related to practice in a rural area. Secondary aims were to capture a more precise understanding of the demographic characteristics of the healthcare professions workforce in Utah (USA) and to assess the viability of ML as a predictive tool. This study incorporated four datasets - the 2017 dental workforce, the 2016 physician workforce, the 2014 nursing workforce and the 2017 pharmacy workforce - collected by the Utah Medical Education Council. Supervised ML techniques were used to identify factors associated with practice location, the outcome variable of interest. The study sample consisted of 11 259 healthcare professionals with an average age of 46.6 years, of which 36.6% were males and 94.5% Caucasian. Four ML methods were applied to assess model performance by comparing accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve. Of the methods used, support vector machine performed the best (accuracy 99.7%, precision 100%, sensitivity 100%, specificity 99.4% and ROC 0.997). The models identified income and rural upbringing as the top factors associated with rural practice. By far, income emerged as the most important factor associated with rural practice, suggesting that attractive income offers might help rural communities address health professional shortages. Rural upbringing was the next most important predictive factor, validating and updating earlier research. The performance of the ML algorithms suggests their usefulness as a tool to model other databases for individualized prediction.

Forecasting US Equity and Bond Correlation—A Machine Learning Approach

The stock–bond correlation is a cornerstone of every asset allocation decision, but estimating it reliably can prove to be challenging given the potential for co-movements to fluctuate significantly based on economic conditions. Using supervised machine learning techniques, this article presents a new approach for identifying key determinants of the correlation between US equity and bond returns, ultimately finding that inflation, alongside real yields, equity volatility, economic growth, and inflation uncertainty, predict changes in correlation dynamics overtime. Relative to the existing literature, the authors’ approach allows for the systematic detection of the main drivers of stock–bond correlation and uncovers the time variation in importance of each determinant across economic regimes. Upon conducting an out-of-sample portfolio evaluation, the authors show that the five factors with gradient boosting regression approach outperforms all other existing factor-based models in estimating both the trend and level of correlation, thereby offering an alternative robust solution for forecasting time-varying equity and bond co-movements that can be further applied to asset allocation decisions and risk management.

Soil Health Prediction Using Supervised Machine Learning Technique

Abstract: Agriculture is one of the major fields in India that has been overlooked by technical touch. The application of artificial intelligence derivatives such as machine learning and deep learning to agricultural practises aids in crop production and soil health maintenance. The health of an agricultural field is primarily concerned with the preservation of soil nutrients, such as chemical and physical properties, by properly transmitting supplements. When soil health is managed scientifically, it gradually aids in high yield production and the long life of cultivation land. The soil data collected from soil testing centres is used to build the ontology. Ontology is constructed in such a way that it demonstrates the knowledge and relationship between soil and its chemical nutrients. The knowledge base is then used to connect the nutrient and soil type. Machine learning comes with useful and best-in-class algorithms for managing soil health and classifying it into healthy and unhealthy categories. In this study, obvious machine learning algorithms are used to efficiently classify the soil into two classes: healthy and unhealthy. To classify the data, algorithms such as logistic regression, Decision tree, Random tree classifier, Support Vector Machine, and XGBoost were used, and their algorithmic efficiency was increased through hyper parameter tuning using various techniques. Keywords: Soil health, chemical fertility, Supervised Learning, SVM, Decision Tree, Logistic Regression, Ensemble technique

Laptop Price Prediction using Machine Learning

This paper presents a Laptop price prediction system by using the supervised machine learning technique. The research uses multiple linear regression as the machine learning prediction method which offered 81% prediction precision. Using multiple linear regression, there are multiple independent variables but one and only one dependent variable whose actual and predicted values are compared to find precision of results. This paper proposes a system where price is dependent variable which is predicted, and this price is derived from factors like Laptop’s model, RAM, ROM (HDD/SSD), GPU, CPU, IPS Display, and Touch Screen.