Exploring the Machine Learning Algorithm for Prediction the Loan Sanctioning Process

Extending credits to corporates and individuals for the smooth functioning of growing economies like India is inevitable. As increasing number of customers apply for loans in the banks and non- banking financial companies (NBFC), it is really challenging for banks and NBFCs with limited capital to device a standard resolution and safe procedure to lend money to its borrowers for their financial needs. In addition, in recent times NBFC inventories have suffered a significant downfall in terms of the stock price. It has contributed to a contagion that has also spread to other financial stocks, adversely affecting the benchmark in recent times. In this paper, an attempt is made to condense the risk involved in selecting the suitable person who could repay the loan on time thereby keeping the bank’s non-performing assets (NPA) on the hold. This is achieved by feeding the past records of the customer who acquired loans from the bank into a trained machine learning model which could yield an accurate result. The prime focus of the paper is to determine whether or not it will be safe to allocate the loan to a particular person. This paper has the following sections (i) Collection of Data, (ii) Data Cleaning and (iii) Performance Evaluation. Experimental tests found that the Naïve Bayes model has better performance than other models in terms of loan forecasting.

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Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽

10.3390/e23030300 ◽

2021 ◽

Vol 23 (3) ◽

pp. 300

Author(s):

Mark Lokanan ◽

Susan Liu

Keyword(s):

Machine Learning ◽

Financial Literacy ◽

Learning Algorithm ◽

Demographic Characteristics ◽

Financial Knowledge ◽

The Past ◽

Machine Learning Model ◽

Long Time ◽

Regulatory Organization ◽

Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

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Automating Well Log Correlation Workflow Using Soft Attention Convolutional Neural Networks

10.2118/205985-ms ◽

2021 ◽

Author(s):

Aria Abubakar ◽

Mandar Kulkarni ◽

Anisha Kaul

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Gamma Ray ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Well Log ◽

Machine Learning Model ◽

Well Correlation ◽

Geological Formations

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.

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Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

Journal of Healthcare Engineering ◽

10.1155/2021/1302989 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Mohammad Nahid Hossain ◽

Mohammad Helal Uddin ◽

K. Thapa ◽

Md Abdullah Al Zubaer ◽

Md Shafiqul Islam ◽

...

Keyword(s):

Machine Learning ◽

Older Adults ◽

Cognitive Impairment ◽

Learning Algorithm ◽

Negative Impact ◽

Gradient Boosting ◽

Feature Selection Technique ◽

Activity Data ◽

Physical Data ◽

Machine Learning Model

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

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A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

10.1101/204081 ◽

2017 ◽

Cited By ~ 2

Author(s):

Aymen A. Elfiky ◽

Maximilian J. Pany ◽

Ravi B. Parikh ◽

Ziad Obermeyer

Keyword(s):

Machine Learning ◽

Mortality Risk ◽

Palliative Chemotherapy ◽

Learning Algorithm ◽

Cancer Center ◽

Short Term ◽

Machine Learning Model ◽

Machine Learning Approach ◽

Short Term Mortality ◽

And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

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Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

10.21203/rs.3.rs-196077/v1 ◽

2021 ◽

Author(s):

Praveeen Anandhanathan ◽

Priyanka Gopalan

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Machine Learning Techniques ◽

Support Vector ◽

Nearest Neighbour ◽

Decision Tree Algorithm ◽

The Past ◽

Random Forest Method ◽

Learning Techniques ◽

The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

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CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

10.1101/2021.11.12.21266286 ◽

2021 ◽

Author(s):

jorge cabrera Alvargonzalez ◽

Ana Larranaga Janeiro ◽

Sonia Perez ◽

Javier Martinez Torres ◽

Lucia martinez lamas ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Supervised Classification ◽

Learning Algorithm ◽

Support Vector ◽

Classification Algorithms ◽

Machine Learning Algorithm ◽

Proof Of Concept ◽

The Past ◽

Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

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ONLINE LEARNING FOR IMAGE PROCESSING IN NETWORKED SETTING

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19738 ◽

2017 ◽

Vol 10 (13) ◽

pp. 284

Author(s):

Ankush Rai ◽

Jagadeesh Kannan R

Keyword(s):

Machine Learning ◽

Image Processing ◽

Online Learning ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

The Past ◽

Slow Development ◽

Online Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency. In this study we present a novel online learning algorithm for network based computational operations in image processing setting

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A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection

Molecules ◽

10.3390/molecules25112487 ◽

2020 ◽

Vol 25 (11) ◽

pp. 2487 ◽

Cited By ~ 2

Author(s):

José Jiménez-Luna ◽

Alberto Cuzzolin ◽

Giovanni Bolcato ◽

Mattia Sturlese ◽

Stefano Moro

Keyword(s):

Machine Learning ◽

Ligand Docking ◽

Learning Approach ◽

Small Compound ◽

The Past ◽

Machine Learning Model ◽

Docking Protocol ◽

Different Types ◽

Protocol Selection ◽

Fully Connected

While a plethora of different protein–ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein–ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein–ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein–ligand pair.

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Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer

Sensors ◽

10.3390/s20154299 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4299 ◽

Cited By ~ 2

Author(s):

Eui Jung Moon ◽

Youngsik Kim ◽

Yu Xu ◽

Yeul Na ◽

Amato J. Giaccia ◽

...

Keyword(s):

Machine Learning ◽

Spectral Data ◽

Near Infrared ◽

Learning Algorithm ◽

Spectral Response ◽

Machine Learning Algorithm ◽

Simple Method ◽

Machine Learning Model ◽

Multiple Devices ◽

Food Freshness

There has been strong demand for the development of an accurate but simple method to assess the freshness of food. In this study, we demonstrated a system to determine food freshness by analyzing the spectral response from a portable visible/near-infrared (VIS/NIR) spectrometer using the Convolutional Neural Network (CNN)-based machine learning algorithm. Spectral response data from salmon, tuna, and beef incubated at 25 °C were obtained every minute for 30 h and then categorized into three states of “fresh”, “likely spoiled”, and “spoiled” based on time and pH. Using the obtained spectral data, a CNN-based machine learning algorithm was built to evaluate the freshness of experimental objects. In addition, a CNN-based machine learning algorithm with a shift-invariant feature can minimize the effect of the variation caused using multiple devices in a real environment. The accuracy of the obtained machine learning model based on the spectral data in predicting the freshness was approximately 85% for salmon, 88% for tuna, and 92% for beef. Therefore, our study demonstrates the practicality of a portable spectrometer in food freshness assessment.

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