scholarly journals Building ML Based Intelligent System to Analyze Production LSI (Live Site Incidents)

2021 ◽  
Vol 10 (3) ◽  
pp. 41-46
Author(s):  
Himanshu Bajpai
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Intelligent System ◽  
Learning Model ◽  
Classification Model ◽  
Classification Algorithms ◽  
End User ◽  
Machine Learning Model ◽  
Basic Understanding ◽  
Major Factors

Providing support on the rolled-out application/services is one of the major factors in increasing the customer satisfaction which in turn increases the customer retention. Since we are in the era of automation where most of the day-to-day jobs are taken care of or are facilitated by the technologies around us, hence there is a need to reduce manual effort in triaging the support tickets and hence facilitating the person on call to better close the tickets on time with proper remediation. The machine learning model which will be the product of this complete paper will not only help in classifying the tickets but also, if applicable will give the best possible remediation of the ticket there by reducing the manual effort and the time taken on providing necessary solution on the ticket. The objectives of the work are as follows - a) Understand the data that is present in the ticket and figure out the basic understanding like, categories of issues, trends etc. b) Prepare the data which is ready for applying different classification algorithms. d) Identify the best machine learning model which can classify the new incident with utmost accuracy. e) Prepare a machine learning model which can suggest the best possible remediation of the ticket. f) Integrate the best classification model and solution recommender model and wrap it as an API which can be used by end user.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

scholarly journals A Multilayer Hybrid Machine Learning Model for Diabetes Detection

2020 ◽  
Vol 32 ◽  
pp. 03032
Author(s):  
Sahil Parab ◽  
Piyush Rathod ◽  
Durgesh Patil ◽  
Vishwanath Chikkareddi
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Case Scenario ◽  
Detection Model ◽  
Machine Learning Model ◽  
Hybrid Machine

Diabetes Detection has been one of the many challenges which is being faced by the medical as well as technological communities. The principles of machine learning and its algorithms is used in order to detect the possibility of a diabetic patient based on their level of glucose concentration , insulin levels and other medically point of view required test reports. The basic diabetes detection model uses Bayesian classification machine learning algorithm, but even though the model is able to detect diabetes, the efficiency is not acceptable at all times because of the drawbacks of the single algorithm of the model. A Hybrid Machine Learning Model is used to overcome the drawbacks produced by a single algorithm model. A Hybrid Model is constructed by implementing multiple applicable machine learning algorithms such as the SVM model and Bayesian’s Classification model or any other models in order to overcome drawbacks faced by each other and also provide their mutually contributed efficiency. In a perfect case scenario the new hybrid machine learning model will be able to provide more efficiency as compared to the old Bayesian’s classification model.

scholarly journals Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0240200
Author(s):  
Miguel Marcos ◽  
Moncef Belhassen-García ◽  
Antonio Sánchez-Puente ◽  
Jesús Sampedro-Gomez ◽  
Raúl Azibeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Disease Progression ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Learning Model ◽  
Classification Model ◽  
Blood Oxygen Saturation ◽  
Severity Of Disease ◽  
Machine Learning Model

Background Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. Methods We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. Results A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. Conclusions This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

scholarly journals Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

2020 ◽  
Author(s):  
Miguel Marcos ◽  
Moncef Belhassen-Garcia ◽  
Antonio Sanchez- Puente ◽  
Jesus Sampedro-Gomez ◽  
Raul Azibeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Disease Progression ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Learning Model ◽  
Classification Model ◽  
Blood Oxygen Saturation ◽  
Severity Of Disease ◽  
Machine Learning Model

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

scholarly journals Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany

2018 ◽  
Vol 20 (5) ◽  
pp. 1131-1147 ◽  
Author(s):  
N. Caradot ◽  
M. Riechel ◽  
M. Fesneau ◽  
N. Hernandez ◽  
A. Torres ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Learning Model ◽  
Decision Makers ◽  
End User ◽  
User Perspective ◽  
Sewer Pipes ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Better Than

Abstract Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe, whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.

scholarly journals Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

2021 ◽  
Vol 11 (24) ◽  
pp. 11735
Author(s):  
Seungheon Chae ◽  
Ahnryul Choi ◽  
Hyunwoo Jung ◽  
Tae Hyong Kim ◽  
Kyungran Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Musculoskeletal Diseases ◽  
Lower Extremities ◽  
Learning Model ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Joint Moments ◽  
Machine Learning Model ◽  
Starting Point

Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.

scholarly journals Prediction of Credit Card Approval

2022 ◽  
Vol 11 (2) ◽  
pp. 1-6
Author(s):  
Harsha Vardhan Peela ◽  
◽  
Tanuj Gupta ◽  
Nishit Rathod ◽  
Tushar Bose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Learning Model ◽  
Card Holder ◽  
Great Expectations ◽  
Machine Learning Model ◽  
Using Data ◽  
Major Factors ◽  
Tools And Techniques ◽  
Python Programming

Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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