scholarly journals Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 17
Author(s):  
Fazlollah Soleymani ◽  
Houman Masnavi ◽  
Stanford Shateyi
Keyword(s):  
Machine Learning ◽  
Mathematical Finance ◽  
Bankruptcy Prediction ◽  
Computational Time ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Dynamic Updates

Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.

scholarly journals Machine Learning Approach to Dysphonia Detection

2018 ◽  
Vol 8 (10) ◽  
pp. 1927 ◽  
Author(s):  
Zuzana Dankovičová ◽  
Dávid Sovák ◽  
Peter Drotár ◽  
Liberios Vokorokos
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Speech Features

This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

scholarly journals SENTIMENT ANALYSIS OF COVID-19 TWEETS

2021 ◽  
Vol 5 (1) ◽  
pp. 566-576
Author(s):  
Azeez A. Nureni ◽  
Victor E. Ogunlusi ◽  
Emmanuel Junior Uloko
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Global Pandemic ◽  
Machine Learning Approach

Sentiment analysis involves techniques used in analyzing texts in order to identify the sentiment and emotion dominant in such texts and classify them accordingly. Techniques involved include but not limited to preprocessing of texts and the use a machine learning or lexical based approach in classifying these texts. In this research, attempt was made to adopt a machine learning approach to classify tweets on Covid-19 which is considered a global pandemic. To achieve this noble objective, a cross-dataset approach was applied to train four machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), as well as K-Nearest Neighbors algorithm (KNN). The final result will not only assist us in knowing the best performing algorithm, it will also assist in creating awareness on Covid-19 with the final objective of destigmatizing the patients through the analysis of sentiments and emotions on Covid-19  and finally use the same result for containing the spread of the pandemic

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2328 ◽  
Author(s):  
Md Shafiullah ◽  
M. Abido ◽  
Taher Abdel-Fattah
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Approach ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Performance Indices ◽  
Machine Learning Approach ◽  
Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

scholarly journals Thermal mapp routing in pharmaceutical products transportation using machine learning approach: a systematic review

2021 ◽  
Vol 10 (16) ◽  
pp. e170101623665
Author(s):  
Clayton Gerber Mangini ◽  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Pharmaceutical Products ◽  
Cold Chain ◽  
Machine Learning Technique ◽  
Thermal Mapping ◽  
Extensive Field ◽  
Machine Learning Approach ◽  
Learning Technique ◽  
Main Factors

The cold chain is crucial to ensure the quality and effectiveness of transported and stored medicines. For this, it is necessary to carry out the thermal mapping of routes for drugs transported between 15°C and 30°C, so that the most assertive decision can be taken without raising costs. This study aims to identify the main factors influencing the thermal mapping of pharmaceutical products in the cold chain and applying the machine learning technique. The method used for this systematic review is the Prisma, where the identification, screening, eligibility, and inclusion stages were analyzed. After analyzing 75 articles, the result shows that only eight papers were consistent with the use of modeling in the medicine cold chain distribution. Thus, it can be concluded that there is an extensive field to be researched regarding the use of prediction algorithms in the cold chain of drugs and vaccines.

scholarly journals A machine learning approach of finding the optimal anisotropic SPH kernel

2021 ◽  
Vol 2090 (1) ◽  
pp. 012115
Author(s):  
Eraldo Pereira Marinho
Keyword(s):  
Machine Learning ◽  
Convex Hull ◽  
Compact Support ◽  
Nearest Neighbors ◽  
The Self ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Approach

Abstract It is presented a machine learning approach to find the optimal anisotropic SPH kernel, whose compact support consists of an ellipsoid that matches with the convex hull of the self-regulating k-nearest neighbors of the smoothing particle (query).

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals A machine learning approach to predict pancreatic islet grafts rejection versus tolerance

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241925
Author(s):  
Gerardo A. Ceballos ◽  
Luis F. Hernandez ◽  
Daniel Paredes ◽  
Luis R. Betancourt ◽  
Midhat H. Abdulreda
Keyword(s):  
Machine Learning ◽  
Pancreatic Islet ◽  
Support Vector ◽  
Medical Diagnoses ◽  
Laser Induced Fluorescence Detection ◽  
Classification Score ◽  
New Information ◽  
Islet Allografts ◽  
Machine Learning Approach ◽  
Positive Classification

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

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