This document discusses using machine learning models to predict health insurance costs. It examines using linear regression models like simple linear regression, multiple linear regression, and polynomial regression. Simple linear regression uses one independent variable to predict a dependent variable, while multiple linear regression uses multiple independent variables. Polynomial regression fits curves rather than straight lines when relationships are non-linear. The document reviews previous studies on predicting medical costs and sentiment analysis of tweets about health insurance. It then describes the methodology used, focusing on choosing appropriate regression models to predict insurance costs based on various factors.
The document discusses data mining and classification techniques. It defines data mining as the extraction of interesting patterns from large amounts of data. Classification involves using attributes of records in a training dataset to predict the class of new, unseen records. Decision trees are a common classification technique that use attributes to recursively split data into subgroups until each subgroup belongs to a single class. The document also discusses clustering, which organizes unlabeled data into groups without predefined classes.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
U.S. Road Accidents Data Analysis and VisualizationMrinalini Sundar
With the US accident rate as a case study, we are showing you how automated, code-free integration for data housed in major platforms to Azure/Snowflake/Amazon Redshift or Google BigQuery is super easy with Datom.ai. All data transfers are done with a drag and drop interface and are based on a transparent pricing mechanism based on actual usage.
This document summarizes a technical seminar presentation on the design and implementation of a rescue system for women's safety. The system includes a wearable unit with a camera, RF module and switch circuit. A handheld unit includes an RF module, controller and GPS receiver. The system allows for image streaming from the wearable camera unit to a web page. It also provides location tracking of the wearable unit using GPS. The hardware includes an Arduino, GPS module, GSM module and webcam. The software is programmed using Arduino for location tracking and Rasberry Pi for image streaming. Testing showed the location tracking and image streaming components worked as intended. The system aims to provide safety monitoring and alert services for women.
Internship report on AI , ML & IIOT and project responses full docsRakesh Arigela
The internship was conducted at Cognibot, a company that develops AI, machine learning, and IIoT systems. The internship objectives were to understand these technologies and their applications. The intern worked on projects involving home robots, emergency response robots, biomedical research, and FMCG manufacturing. Methodologies used included hierarchical control structures and component-based software development. The intern gained skills in Python programming, machine learning algorithms, and LabVIEW. Challenges included inconsistencies in product data. Benefits to the company include increasing its profile and community through reports on its work applying AI and robotics technologies.
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
The document discusses data mining and classification techniques. It defines data mining as the extraction of interesting patterns from large amounts of data. Classification involves using attributes of records in a training dataset to predict the class of new, unseen records. Decision trees are a common classification technique that use attributes to recursively split data into subgroups until each subgroup belongs to a single class. The document also discusses clustering, which organizes unlabeled data into groups without predefined classes.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
U.S. Road Accidents Data Analysis and VisualizationMrinalini Sundar
With the US accident rate as a case study, we are showing you how automated, code-free integration for data housed in major platforms to Azure/Snowflake/Amazon Redshift or Google BigQuery is super easy with Datom.ai. All data transfers are done with a drag and drop interface and are based on a transparent pricing mechanism based on actual usage.
This document summarizes a technical seminar presentation on the design and implementation of a rescue system for women's safety. The system includes a wearable unit with a camera, RF module and switch circuit. A handheld unit includes an RF module, controller and GPS receiver. The system allows for image streaming from the wearable camera unit to a web page. It also provides location tracking of the wearable unit using GPS. The hardware includes an Arduino, GPS module, GSM module and webcam. The software is programmed using Arduino for location tracking and Rasberry Pi for image streaming. Testing showed the location tracking and image streaming components worked as intended. The system aims to provide safety monitoring and alert services for women.
Internship report on AI , ML & IIOT and project responses full docsRakesh Arigela
The internship was conducted at Cognibot, a company that develops AI, machine learning, and IIoT systems. The internship objectives were to understand these technologies and their applications. The intern worked on projects involving home robots, emergency response robots, biomedical research, and FMCG manufacturing. Methodologies used included hierarchical control structures and component-based software development. The intern gained skills in Python programming, machine learning algorithms, and LabVIEW. Challenges included inconsistencies in product data. Benefits to the company include increasing its profile and community through reports on its work applying AI and robotics technologies.
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
The document discusses the potential applications of deep learning in healthcare. It begins by explaining that deep learning models can improve accuracy of diagnosis, prognosis, and risk prediction by analyzing large datasets. It then discusses how deep learning can optimize hospital processes like resource allocation and patient flow by early and accurate prediction of diseases. Finally, it mentions that deep learning can help identify patient subgroups for personalized and precision medicine approaches.
This document discusses classifying handwritten digits using the MNIST dataset with a simple linear machine learning model. It begins by introducing the MNIST dataset of images and corresponding labels. It then discusses using a linear model with weights and biases to make predictions for each image. The weights represent a filter to distinguish digits. The model is trained using gradient descent to minimize the cross-entropy cost function by adjusting the weights and biases based on batches of training data. The goal is to improve the model's ability to correctly classify handwritten digit images.
Loan approval prediction based on machine learning approachEslam Nader
This document discusses using machine learning models to predict loan approvals. It introduces the motivation, problem statement, and objectives of building a loan prediction system. The document describes the dataset used, which contains information about previous loan applicants. It then explains three machine learning models tested for the predictions: decision tree classifier, logistic regression, and naive Bayesian classifier. The document concludes by reporting the accuracy scores from experimenting with each model, with decision tree performing best.
Credit card fraud detection using python machine learningSandeep Garg
COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
The document describes a project to detect fake news using machine learning models. It discusses how the project classified news websites as real or fake using a combination of bag-of-words, word embeddings and feature descriptions with 87.39% accuracy. Some ways to improve the model are also provided, such as using more features in the word embeddings. Real-world applications of fake news detection include verifying news on social media during elections and detecting fake job postings.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Computer vision is the goal of writing programs that can interpret images, such as video sequences or medical scans. It involves acquiring images, preprocessing them, extracting features, detecting/segmenting objects, and recognizing/interpreting the images. Computer vision draws from fields like calculus, linear algebra, and statistics. It has applications in areas like robotics, navigation, inspection, and medical imaging. While computer vision has improved, it still lacks the subtlety and versatility of human vision.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
Credit card fraud detection using machine learning Algorithmsankit panigrahy
This document discusses credit card fraud detection using machine learning techniques. It compares the performance of naïve bayes, k-nearest neighbor, and logistic regression classifiers on a credit card transactions dataset. The dataset contains over 284,000 transactions with 0.172% fraudulent cases, making the data highly imbalanced. Different resampling techniques are used to address this imbalance. The performance of the classifiers is evaluated based on various metrics like accuracy, sensitivity, specificity, and F1 score. The results show that kNN performs best for most metrics except accuracy on a specific class distribution, while naïve bayes and logistic regression also achieve good performance.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
The document discusses machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It describes several machine learning algorithms like decision trees, k-nearest neighbors, naive bayes, and support vector machines that are used in supervised learning. Unsupervised learning techniques like clustering, association, and k-means clustering are also covered. The document concludes that machine learning approaches can help with systematic reviews by assisting in document screening and improving reviewer agreement.
Introduction to Econometrics for under gruadute class.pptxtadegebreyesus
1) Econometrics is the application of statistical and mathematical techniques to analyze economic data and test economic theories. This document discusses the process of econometric modeling and analysis.
2) Regression analysis is used to estimate the average value of a dependent variable based on the fixed values of independent variables. It allows testing economic theories using actual data.
3) Estimating parameters involves obtaining data, running regressions using techniques like ordinary least squares, and evaluating the results based on economic and statistical criteria.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
The document discusses the potential applications of deep learning in healthcare. It begins by explaining that deep learning models can improve accuracy of diagnosis, prognosis, and risk prediction by analyzing large datasets. It then discusses how deep learning can optimize hospital processes like resource allocation and patient flow by early and accurate prediction of diseases. Finally, it mentions that deep learning can help identify patient subgroups for personalized and precision medicine approaches.
This document discusses classifying handwritten digits using the MNIST dataset with a simple linear machine learning model. It begins by introducing the MNIST dataset of images and corresponding labels. It then discusses using a linear model with weights and biases to make predictions for each image. The weights represent a filter to distinguish digits. The model is trained using gradient descent to minimize the cross-entropy cost function by adjusting the weights and biases based on batches of training data. The goal is to improve the model's ability to correctly classify handwritten digit images.
Loan approval prediction based on machine learning approachEslam Nader
This document discusses using machine learning models to predict loan approvals. It introduces the motivation, problem statement, and objectives of building a loan prediction system. The document describes the dataset used, which contains information about previous loan applicants. It then explains three machine learning models tested for the predictions: decision tree classifier, logistic regression, and naive Bayesian classifier. The document concludes by reporting the accuracy scores from experimenting with each model, with decision tree performing best.
Credit card fraud detection using python machine learningSandeep Garg
COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.
Breast cancer diagnosis machine learning pptAnkitGupta1476
This document summarizes a presentation on using machine learning to diagnose breast cancer. It introduces machine learning and explains that it uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. It then provides an overview of breast cancer, risk factors, and statistics. It states that machine learning will be used to analyze breast cancer biopsy data to make diagnoses. The document outlines the steps of collecting and exploring the biopsy data, preparing training and test datasets, training a k-nearest neighbors model on the data, and calculating the model's accuracy on the test data using a confusion matrix.
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
The document describes a project to detect fake news using machine learning models. It discusses how the project classified news websites as real or fake using a combination of bag-of-words, word embeddings and feature descriptions with 87.39% accuracy. Some ways to improve the model are also provided, such as using more features in the word embeddings. Real-world applications of fake news detection include verifying news on social media during elections and detecting fake job postings.
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Computer vision is the goal of writing programs that can interpret images, such as video sequences or medical scans. It involves acquiring images, preprocessing them, extracting features, detecting/segmenting objects, and recognizing/interpreting the images. Computer vision draws from fields like calculus, linear algebra, and statistics. It has applications in areas like robotics, navigation, inspection, and medical imaging. While computer vision has improved, it still lacks the subtlety and versatility of human vision.
Using prior knowledge to initialize the hypothesis,kbannswapnac12
1) The KBANN algorithm uses a domain theory represented as Horn clauses to initialize an artificial neural network before training it with examples. This helps the network generalize better than random initialization when training data is limited.
2) KBANN constructs a network matching the domain theory's predictions exactly, then refines it with backpropagation to fit examples. This balances theory and data when they disagree.
3) In experiments on promoter recognition, KBANN achieved a 4% error rate compared to 8% for backpropagation alone, showing the benefit of prior knowledge.
Credit card fraud detection using machine learning Algorithmsankit panigrahy
This document discusses credit card fraud detection using machine learning techniques. It compares the performance of naïve bayes, k-nearest neighbor, and logistic regression classifiers on a credit card transactions dataset. The dataset contains over 284,000 transactions with 0.172% fraudulent cases, making the data highly imbalanced. Different resampling techniques are used to address this imbalance. The performance of the classifiers is evaluated based on various metrics like accuracy, sensitivity, specificity, and F1 score. The results show that kNN performs best for most metrics except accuracy on a specific class distribution, while naïve bayes and logistic regression also achieve good performance.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
The document discusses machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It describes several machine learning algorithms like decision trees, k-nearest neighbors, naive bayes, and support vector machines that are used in supervised learning. Unsupervised learning techniques like clustering, association, and k-means clustering are also covered. The document concludes that machine learning approaches can help with systematic reviews by assisting in document screening and improving reviewer agreement.
Introduction to Econometrics for under gruadute class.pptxtadegebreyesus
1) Econometrics is the application of statistical and mathematical techniques to analyze economic data and test economic theories. This document discusses the process of econometric modeling and analysis.
2) Regression analysis is used to estimate the average value of a dependent variable based on the fixed values of independent variables. It allows testing economic theories using actual data.
3) Estimating parameters involves obtaining data, running regressions using techniques like ordinary least squares, and evaluating the results based on economic and statistical criteria.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
Presentation by U. Devrim Demirel, CBO's Fiscal Policy Studies Unit Chief, and James Otterson at the 28th International Conference of The Society for Computational Economics.
This document describes a major project aimed at predicting health insurance costs using regression models. The objectives are to implement efficient algorithms that provide accurate predictions and to compare different regression algorithms. The project will use multiple linear regression, decision tree regression, and gradient boosting regression on health insurance data to predict costs. Literature on using machine learning and deep learning models for health insurance cost prediction is reviewed. The hardware, software, methods, and key concepts of multiple linear regression, decision tree regression, and gradient boosting regression are described.
Predictive analytics uses techniques like data mining, machine learning, and statistics to make predictions about unknown future events. There are two main predictive analytics methods: regression and classification. Regression models predict continuous variables like revenue or time until failure, using techniques like linear regression which finds the best-fit straight line relationship between variables. Classification models predict class membership, such as whether a customer will leave or a credit risk is good, usually resulting in a 0 or 1 prediction. Predictive analytics has applications in industries like banking, retail, government, manufacturing, and healthcare.
A Medical Price Prediction System using Boosting Algorithms through Machine L...IRJET Journal
This document presents research on using machine learning algorithms to predict medical insurance costs. The researchers trained and tested various regression algorithms on health insurance data to predict premium costs, including linear regression, random forest, and gradient boosting. Random forest regression performed best with an r2 score of 0.862533. Comparing actual and predicted costs through algorithms can help insurance companies choose the most accurate model for predicting customer premiums and improving medical access. The researchers conclude random forest was the best algorithm identified for accurately predicting health insurance costs.
Modelling Inflation using Generalized Additive Mixed Models (GAMM)AI Publications
Inflation becomes an important thing to become a benchmark for economic growth, investor considerations factor in choosing the type of investment, as well as determining factors for the government in formulating fiscal policy, monetary or non-monetary to be run. Inflation calculations carried out using the Consumer Price Index, known as CPI as an indicator to measure the cost of consumption of goods and services markets. Based on an analysis using GAMM was concluded R2 value of 0.996 or can be interpreted that the inflation amounted to 99.6 % can be explained by the variables used in this study and 0.4 % is explained by other factors
https://ijitce.com/index.php
Our journal maintains rigorous peer review standards. Each submitted article undergoes a thorough evaluation by experts in the respective field. This stringent review process helps ensure that only high-quality and scientifically sound research is accepted for publication. Researchers can trust that the articles they find in IJITCE have been critically assessed for validity, significance, and originality.
This paper provides a structured way of thinking the design and construction of a composite indicator whose purpose is to facilitate ranking EU countries by Health System Performance and/or assess their progress over time on complex and multi-dimensional health issues.
Assigning Scores For Ordered Categorical ResponsesMary Montoya
This document summarizes a research article that proposes a new method for assigning scores to ordered categorical response variables in statistical analysis. Specifically, it discusses the ordered stereotype model, which allows for uneven spacing between categories of an ordinal variable through estimated score parameters. The article presents simulation studies showing the disadvantages of assuming equal spacing, and applies the ordered stereotype model to a real dataset, demonstrating non-equal spacing. It also proposes a new median measure for ordinal data based on estimated score parameters from the ordered stereotype model.
INFLUENCE OF THE EVENT RATE ON DISCRIMINATION ABILITIES OF BANKRUPTCY PREDICT...ijdms
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this
bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy
prediction models. First the statistical association and significance of public records and firmographics
indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%,
20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression,
Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. Under different event rates, models were comprehensively evaluated and compared based
on Kolmogorov-Smirnov Statistic, accuracy,F1 score, Type I error, Type II error, and ROC curve on the
hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most
insensitive to the event rate, while Support Vector Machine is the most sensitive.
Welcome to this comprehensive presentation on regression analysis, a fundamental technique in predictive modeling. In this slide deck, we will embark on a journey through the intricate world of regression, exploring its essence, types, applications, systematic process, underlying assumptions, diagnostic tools, and real-world significance.
Regression analysis is a powerful statistical tool that enables us to understand and quantify the relationships between variables. By examining the interplay between a dependent variable and one or more independent variables, regression unveils patterns and trends that can drive informed decision-making. Whether you're working in finance, marketing, healthcare, or any other field, regression empowers analysts to extract valuable information from their data and make accurate predictions.
During our exploration, we will delve into various types of regression models. Simple Linear Regression establishes a linear relationship between two variables, serving as a foundation for understanding more complex models. Multiple Linear Regression expands this concept by incorporating multiple predictors, allowing us to account for multiple factors influencing the dependent variable. Polynomial Regression goes beyond linear relationships, capturing non-linear associations between variables. Logistic Regression, on the other hand, is specifically designed for predicting categorical outcomes, making it an invaluable tool for classification problems.
Throughout the presentation, we will showcase real-world applications of regression analysis. Witness how regression aids in predicting stock prices, forecasting sales, estimating housing prices, analyzing customer behavior, predicting disease outcomes, optimizing resource allocation, and much more. These examples illustrate the remarkable impact of regression across industries, demonstrating its relevance and effectiveness in solving complex problems and driving data-driven decision-making.
In conclusion, regression analysis is a powerful tool that unlocks a world of possibilities. By unraveling complex relationships, making accurate predictions, and extracting valuable insights from data, regression empowers analysts to drive evidence-based decision-making and stay ahead in a rapidly evolving world. Join us as we delve into the world of regression and discover its potential to transform the way you approach data analysis and modeling. Let's embark on this journey together and harness the power of regression analysis!
A Multi-Pronged Approach to Data Mining Post-Acute Care EpisodesDamian R. Mingle, MBA
This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, specific to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are defined, the financial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present findings through visualizations enabling data discovery and accountability.
Minimum Health Insurance Premium prediction using health parametersIRJET Journal
This document presents a study on using machine learning algorithms to predict minimum health insurance premium amounts based on individuals' health parameters. The study analyzes various health-related attributes like age, diabetes status, surgeries, etc. from datasets to train regression models. Random forest regression is identified as the best performing algorithm with an accuracy of around 85-90%. The proposed system aims to help individuals choose more appropriate insurance amounts by considering a wider range of health factors compared to existing solutions. It describes the system architecture involving data collection, model training, and premium prediction when users input their health details.
Analysis and Estimation of Child Mortality and the Influence of Maternal Care...IRJET Journal
This document summarizes a research project that analyzed data from the National Family Health Survey (NFHS) 2015-16 to determine relationships between infant mortality rate (IMR) and factors of maternal care. Several machine learning regression models were tested including linear regression, multiple linear regression, lasso regression, polynomial regression, and ridge regression. Multiple linear regression was found to produce the most accurate predictions of IMR based on maternal care factors. The results indicate that prenatal care is associated with lower infant mortality rates compared to no prenatal care.
Predicting an Applicant Status Using Principal Component, Discriminant and Lo...inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Review Parameters Model Building & Interpretation and Model Tunin.docxcarlstromcurtis
Review Parameters: Model Building & Interpretation and Model Tuning
1. Model Building
a. Assessments and Rationale of Various Models Employed to Predict Loan Defaults
The z-score formula model was employed by Altman (1968) while envisaging bankruptcy. The model was utilized to forecast the likelihood that an organization may fall into bankruptcy in a period of two years. In addition, the Z-score model was instrumental in predicting corporate defaults. The model makes use of various organizational income and balance sheet data to weigh the financial soundness of a firm. The Z-score involves a Linear combination of five general financial ratios which are assessed through coefficients. The author employed the statistical technique of discriminant examination of data set sourced from publically listed manufacturers. A research study by Alexander (2012) made use of symmetric binary alternative models, otherwise referred to as conditional probability models. The study sought to establish the asymmetric binary options models subject to the extreme value theory in better explicating bankruptcy.
In their research study on the likelihood of default models examining Russian banks, Anatoly et al. (2014) made use of binary alternative models in predicting the likelihood of default. The study established that preface specialist clustering or mechanical clustering enhances the prediction capacity of the models. Rajan et al. (2010) accentuated the statistical default models as well as inducements. They postulated that purely numerical models disregard the concept that an alteration in the inducements of agents who produce the data may alter the very nature of data. The study attempted to appraise statistical models that unpretentiously pool resources on historical figures devoid of modeling the behavior of driving forces that generates these data. Goodhart (2011) sought to assess the likelihood of small businesses to default on loans. Making use of data on business loan assortment, the study established the particular lender, loan, and borrower characteristics as well as modifications in the economic environments that lead to a rise in the probability of default. The results of the study form the basis for the scoring model. Focusing on modeling default possibility, Singhee & Rutenbar (2010) found the risk as the uncertainty revolving around an enterprise’s capacity to service its obligations and debts.
Using the logistic model to forecast the probability of bank loan defaults, Adam et al. (2012) employed a data set with demographic information on borrowers. The authors attempted to establish the risk factors linked to borrowers are attributable to default. The identified risk factors included marital status, gender, occupation, age, and loan duration. Cababrese (2012) employed three accepted data mining algorithms, naïve Bayesian classifiers, artificial neural network decision trees coupled with a logical regression model to formulate a prediction m ...
This document summarizes key concepts in regression analysis for developing cost estimating relationships. Simple regression uses a single independent variable to predict a dependent variable based on a straight line model. The coefficient of determination, standard error of the estimate, and T-test are used to measure how well the regression equation fits the data. Regression is commonly used to establish cost estimating relationships, analyze indirect cost rates over time, and forecast trends while controlling for other influencing factors.
Econometrics combines economic theory, mathematics, and statistical methods to analyze economic data and test hypotheses. It allows economists to quantify economic relationships and forecast future trends. Some key points covered in the document include:
- Econometrics uses statistical methods and economic theory to develop and test economic models and hypotheses about economic relationships using real-world data.
- Important founders of econometrics include Jan Tinbergen and Ragnar Frisch.
- Econometric models specify statistical relationships between economic variables based on economic theory and allow testing of theories and forecasting.
- Data sources include time series data, cross-sectional data, and panel data. Econometrics is useful for
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TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.