The document proposes an ensemble reinforcement learning architecture for portfolio management that combines multiple deep reinforcement learning algorithms. It aims to stabilize training and improve performance by reducing variance in the target approximation error through a temporal ensemble, and reducing overestimation of Q-values by ensembling target values from different algorithms. The proposed ensemble architecture integrates different reinforcement learning methods based on approximate value functions to leverage their individual strengths while avoiding issues like divergence. Evaluation shows the temporal ensemble makes training more stable while ensembling algorithms provides more accurate Q-value estimates, enabling stable convergence.
IRJET- Aspect based Sentiment Analysis on Financial Data using Transferred Le...IRJET Journal
This document presents an approach for aspect classification and sentiment prediction on financial data using transferred learning with BERT and regression models. The authors fine-tune BERT for aspect classification and use linear support vector regression for sentiment prediction, achieving an F1-score of 0.46-0.41 for aspect classification and MSE of 0.36-0.13 for sentiment prediction on the test data. They conclude BERT transfer learning is effective for this task and future work could explore other models like XLNet and larger datasets.
IRJET- Improving Prediction of Potential Clients for Bank Term Deposits using...IRJET Journal
This document summarizes research on improving predictions of potential clients for bank term deposits using machine learning approaches. The researchers analyzed bank customer data using logistic regression, support vector machines, random forests, and XGBoost models. They found that XGBoost performed best with an area under the ROC curve of 0.7368, an F1 score of 0.9291, and test accuracy of 0.8351. The study aimed to identify the most effective predictive model that can be used in bank telemarketing campaigns to target potential clients.
Automation of IT Ticket Automation using NLP and Deep LearningPranov Mishra
Overview of Problem Solved: IT leverages Incident Management process to ensure Business Operations is never impacted. The assignment of incidents to appropriate IT groups is still a manual process in many of the IT organizations. Manual assignment of incidents is time consuming and requires human efforts. There may be mistakes due to human errors and resource consumption is carried out ineffectively because of the misaddressing. Manual assignment increases the response and resolution times which result in user satisfaction deterioration / poor customer service.
Solution: Multiple deep learning sequential models with Glove Embeddings were attempted and results compared to arrive at the best model. The two best models are highlighted below through their results.
1. Bi-Directional LSTM attempted on the data set has given an accuracy of 71% and precision of 71%.
2. The accuracy and precision was further improved to 73% and 76% respectively when an ensemble of 7 Bi-LSTM was built.
I built a NLP based Deep Learning model to solve the above problem. Link below
https://github.com/Pranov1984/Application-of-NLP-in-Automated-Classification-of-ticket-routing?fbclid=IwAR3wgofJNMT1bIFxL3P3IoRC3BTuWmhw1SzAyRtHp8vvj9F2sKZdq67SjDA
Fuzzy Logic Framework for Qualitative Evaluation of Supply Chain Responsivenesstheijes
Fuzzy logic can be a powerful tool for managers to use instead of traditional mathematical models when measuring the of supply chains responsivess. The flexibility of the model allows the decision maker to introduce vagueness, uncertainty, and subjectivity into the evaluation system. Responsiveness measurement represents a critically important decision that often involves subjective information. Fuzzy logic models provide a reasonable solution to these common decision situations. After extensive exploration of the literature, we recommend an outcome of developing a Fuzzy logic framework in measuring qualitative aspects of supply chain responsiveness. In this paper, responsiveness as one of the important factors of measuring qualitative performance is discussed and a fuzzy logic framework is developed to measure supply chain responsiveness.
This document summarizes Salford Systems' participation in an international competition to predict customer churn for a major mobile provider. Salford Systems used an ensemble of decision tree models called TreeNet to predict churn with significantly higher accuracy than other methods. TreeNet models achieved a top decile lift of 3.01 and Gini coefficient of 0.400 on future churn predictions, substantially better than the average and second place method. The document outlines the data and task, TreeNet methodology, results, and conclusions that TreeNet was key to winning due to its superior predictive performance.
Modelling the expected loss of bodily injury claims using gradient boostingGregg Barrett
This document summarizes an effort to model the expected loss of bodily injury claims using gradient boosting. Frequency and severity models are built separately and then combined to estimate expected loss. Gradient boosting is chosen as the modeling approach due to its flexibility. Tuning parameters like shrinkage, number of trees, and depth must be selected. The goal is predictive accuracy over interpretability. Performance is evaluated on a test set not used for model selection.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
IRJET- Aspect based Sentiment Analysis on Financial Data using Transferred Le...IRJET Journal
This document presents an approach for aspect classification and sentiment prediction on financial data using transferred learning with BERT and regression models. The authors fine-tune BERT for aspect classification and use linear support vector regression for sentiment prediction, achieving an F1-score of 0.46-0.41 for aspect classification and MSE of 0.36-0.13 for sentiment prediction on the test data. They conclude BERT transfer learning is effective for this task and future work could explore other models like XLNet and larger datasets.
IRJET- Improving Prediction of Potential Clients for Bank Term Deposits using...IRJET Journal
This document summarizes research on improving predictions of potential clients for bank term deposits using machine learning approaches. The researchers analyzed bank customer data using logistic regression, support vector machines, random forests, and XGBoost models. They found that XGBoost performed best with an area under the ROC curve of 0.7368, an F1 score of 0.9291, and test accuracy of 0.8351. The study aimed to identify the most effective predictive model that can be used in bank telemarketing campaigns to target potential clients.
Automation of IT Ticket Automation using NLP and Deep LearningPranov Mishra
Overview of Problem Solved: IT leverages Incident Management process to ensure Business Operations is never impacted. The assignment of incidents to appropriate IT groups is still a manual process in many of the IT organizations. Manual assignment of incidents is time consuming and requires human efforts. There may be mistakes due to human errors and resource consumption is carried out ineffectively because of the misaddressing. Manual assignment increases the response and resolution times which result in user satisfaction deterioration / poor customer service.
Solution: Multiple deep learning sequential models with Glove Embeddings were attempted and results compared to arrive at the best model. The two best models are highlighted below through their results.
1. Bi-Directional LSTM attempted on the data set has given an accuracy of 71% and precision of 71%.
2. The accuracy and precision was further improved to 73% and 76% respectively when an ensemble of 7 Bi-LSTM was built.
I built a NLP based Deep Learning model to solve the above problem. Link below
https://github.com/Pranov1984/Application-of-NLP-in-Automated-Classification-of-ticket-routing?fbclid=IwAR3wgofJNMT1bIFxL3P3IoRC3BTuWmhw1SzAyRtHp8vvj9F2sKZdq67SjDA
Fuzzy Logic Framework for Qualitative Evaluation of Supply Chain Responsivenesstheijes
Fuzzy logic can be a powerful tool for managers to use instead of traditional mathematical models when measuring the of supply chains responsivess. The flexibility of the model allows the decision maker to introduce vagueness, uncertainty, and subjectivity into the evaluation system. Responsiveness measurement represents a critically important decision that often involves subjective information. Fuzzy logic models provide a reasonable solution to these common decision situations. After extensive exploration of the literature, we recommend an outcome of developing a Fuzzy logic framework in measuring qualitative aspects of supply chain responsiveness. In this paper, responsiveness as one of the important factors of measuring qualitative performance is discussed and a fuzzy logic framework is developed to measure supply chain responsiveness.
This document summarizes Salford Systems' participation in an international competition to predict customer churn for a major mobile provider. Salford Systems used an ensemble of decision tree models called TreeNet to predict churn with significantly higher accuracy than other methods. TreeNet models achieved a top decile lift of 3.01 and Gini coefficient of 0.400 on future churn predictions, substantially better than the average and second place method. The document outlines the data and task, TreeNet methodology, results, and conclusions that TreeNet was key to winning due to its superior predictive performance.
Modelling the expected loss of bodily injury claims using gradient boostingGregg Barrett
This document summarizes an effort to model the expected loss of bodily injury claims using gradient boosting. Frequency and severity models are built separately and then combined to estimate expected loss. Gradient boosting is chosen as the modeling approach due to its flexibility. Tuning parameters like shrinkage, number of trees, and depth must be selected. The goal is predictive accuracy over interpretability. Performance is evaluated on a test set not used for model selection.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Financial Trading as a Game: A Deep Reinforcement Learning Approach謙益 黃
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used ε-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
Dear students get fully solved SMU MBA assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
This document discusses using an adaptive boosted support vector machine to classify potential direct marketing consumers using bank customer data. It compares the performance of an ordinary SVM classifier to an SVM classifier combined with an Adaboost algorithm. The Adaboost-SVM approach achieved higher accuracy (95.07%) and sensitivity (91.65%) compared to the ordinary SVM (91.67% accuracy and 83.80% sensitivity) in predicting customer subscription prospects from a dataset of over 9,000 records with 20 attributes. The results showed that ensemble methods like Adaboost can improve the performance of a single SVM classifier.
This document summarizes a report on analyzing a stock prediction model using neural networks. The report presents a model that predicts stock prices by extracting stock data, dividing it into training and validation sets, and feeding it into a neural network. Experimental results showed the model could accurately predict stock prices after training on 90% of the data, but predictions on the remaining 10% of data sometimes differed from actual prices. The model allows users to choose different stock attributes or time periods for analysis and prediction.
This document provides information about getting fully solved SMU MBA assignments. It includes the contact information for an assignment help service via email or phone. It also includes sample questions and answers for an MBA Project Management assignment, covering topics like the project lifecycle, economic feasibility, need for project planning, diversity management, risk management, cost estimating tools, procurement process, project execution responsibilities, project termination, quality planning, and project performance evaluation techniques. Students are instructed to send their semester and specialization to the provided email or call the phone number for assignment help.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Hibridization of Reinforcement Learning Agentsbutest
The document discusses reinforcement learning techniques for developing intelligent agents that can learn from interactions with their environment. It provides background on reinforcement learning methods like dynamic programming, Monte Carlo methods, and temporal-difference learning. The paper aims to show how hybridizing classic reinforcement learning agents like SARSA and SARSA(λ) through comparative testing can significantly improve their performance.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
This document discusses genetic algorithms and how they are used for concept learning. It explains that genetic algorithms are inspired by biological evolution and use selection, crossover, and mutation to iteratively update a population of hypotheses. It then describes how genetic algorithms work, including representing hypotheses, genetic operators like crossover and mutation, fitness functions, and selection methods. Finally, it provides an example of a genetic algorithm called GABIL that was used for concept learning tasks.
Genetic algorithms (GA) apply evolutionary approaches to inductive learning problems. GA has been successfully applied to difficult problems like scheduling, traveling salesperson, network routing, and financial marketing. GA initialize a population of potential solutions and use genetic operators like crossover and mutation to create new solutions over multiple iterations, replacing weaker solutions with stronger ones according to a fitness function. This leads to increasingly better approximations of the optimal solution.
IRJET- Analyzing Voting Results using Influence MatrixIRJET Journal
This document discusses analyzing voting results using an influence matrix. It proposes modeling voting outcomes as results from an opinion dynamics process, where opinions evolve according to social influence. It formulates estimating the maximum posteriori opinions and influence matrix from voting data. The influence matrix technique is described to solve fluid flow problems numerically. It demonstrates vote prediction and dynamic results visualization based on the estimated influence matrix. Future work could explore modeling stubborn agents' topic-dependent beliefs instead of independently.
This document outlines the aim, objectives, scope, and structure of a dissertation on using genetic programming to optimize and combine K nearest neighbor classifiers for intrusion detection. The aim is to use genetic programming with the KDD Cup 1999 dataset to develop a numeric classifier that shows improved performance over individual KNN classifiers. The objectives are to determine if a GP-based numeric classifier outperforms individual KNN classifiers, if GP combination techniques produce higher performance than KNN component classifiers, and if heterogeneous KNN classifier combination performs better than homogeneous combination. The document describes the methodology that will be used, including developing an optimal KNN classifier using fitness evaluation in the first phase and combining optimal KNN classifiers based on ROC curves in the second phase.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
The document discusses machine learning algorithms for predicting customer churn in a prepaid mobile network. It presents an overview of supervised and unsupervised learning techniques including support vector machines, k-nearest neighbors, neural networks, decision trees and naive Bayes. The document outlines features for a churn prediction model, describes a demo of the model using different algorithms, and evaluates the classification accuracy and churn rates.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
The document provides information about getting fully solved MBA assignments. It details an assignment for the subject Management Information System (MIS) for MBA Semester 2. The assignment contains 6 questions relating to topics in MIS like data flow diagrams, characteristics and functions of MIS, knowledge based systems, value chain analysis, decision making systems, enterprise resource planning, and artificial intelligence. Students are advised to send their semester and specialization details to the provided email or call the given number to get fully solved assignments.
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
Investment Portfolio Risk Manager using Machine Learning and Deep-Learning.IRJET Journal
This document discusses building a system for predicting portfolio risk using machine learning and deep learning models. It reviews several related works that use techniques like decision trees, genetic algorithms, fuzzy logic, neural networks and sentiment analysis to predict stock performance from historical data and news/social media. The proposed work aims to take various inputs like news, tweets, foreign institutional investor data and historical stock prices to train models that can provide insights on portfolio risk and how stocks may perform. It will use long short-term memory networks with sentiment analysis and compare portfolios and historical data to make accurate predictions.
Financial Trading as a Game: A Deep Reinforcement Learning Approach謙益 黃
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used ε-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
Dear students get fully solved SMU MBA assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
This document discusses using an adaptive boosted support vector machine to classify potential direct marketing consumers using bank customer data. It compares the performance of an ordinary SVM classifier to an SVM classifier combined with an Adaboost algorithm. The Adaboost-SVM approach achieved higher accuracy (95.07%) and sensitivity (91.65%) compared to the ordinary SVM (91.67% accuracy and 83.80% sensitivity) in predicting customer subscription prospects from a dataset of over 9,000 records with 20 attributes. The results showed that ensemble methods like Adaboost can improve the performance of a single SVM classifier.
This document summarizes a report on analyzing a stock prediction model using neural networks. The report presents a model that predicts stock prices by extracting stock data, dividing it into training and validation sets, and feeding it into a neural network. Experimental results showed the model could accurately predict stock prices after training on 90% of the data, but predictions on the remaining 10% of data sometimes differed from actual prices. The model allows users to choose different stock attributes or time periods for analysis and prediction.
This document provides information about getting fully solved SMU MBA assignments. It includes the contact information for an assignment help service via email or phone. It also includes sample questions and answers for an MBA Project Management assignment, covering topics like the project lifecycle, economic feasibility, need for project planning, diversity management, risk management, cost estimating tools, procurement process, project execution responsibilities, project termination, quality planning, and project performance evaluation techniques. Students are instructed to send their semester and specialization to the provided email or call the phone number for assignment help.
Customer Churn Analytics using Microsoft R OpenPoo Kuan Hoong
The document summarizes a presentation on using Microsoft R Open for customer churn analytics. It discusses using machine learning algorithms like logistic regression, support vector machines, and random forests to predict customer churn. It compares the performance of these models on a telecom customer dataset using metrics like confusion matrices and ROC curves. The presentation demonstrates building a churn prediction model in Microsoft R Open and R Tools for Visual Studio.
Dear students get fully solved assignments by professionals
Send your semester & Specialization name to our mail id :
stuffstudy5@gmail.com
or
call us at : 098153-33456
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Hibridization of Reinforcement Learning Agentsbutest
The document discusses reinforcement learning techniques for developing intelligent agents that can learn from interactions with their environment. It provides background on reinforcement learning methods like dynamic programming, Monte Carlo methods, and temporal-difference learning. The paper aims to show how hybridizing classic reinforcement learning agents like SARSA and SARSA(λ) through comparative testing can significantly improve their performance.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
This document discusses genetic algorithms and how they are used for concept learning. It explains that genetic algorithms are inspired by biological evolution and use selection, crossover, and mutation to iteratively update a population of hypotheses. It then describes how genetic algorithms work, including representing hypotheses, genetic operators like crossover and mutation, fitness functions, and selection methods. Finally, it provides an example of a genetic algorithm called GABIL that was used for concept learning tasks.
Genetic algorithms (GA) apply evolutionary approaches to inductive learning problems. GA has been successfully applied to difficult problems like scheduling, traveling salesperson, network routing, and financial marketing. GA initialize a population of potential solutions and use genetic operators like crossover and mutation to create new solutions over multiple iterations, replacing weaker solutions with stronger ones according to a fitness function. This leads to increasingly better approximations of the optimal solution.
IRJET- Analyzing Voting Results using Influence MatrixIRJET Journal
This document discusses analyzing voting results using an influence matrix. It proposes modeling voting outcomes as results from an opinion dynamics process, where opinions evolve according to social influence. It formulates estimating the maximum posteriori opinions and influence matrix from voting data. The influence matrix technique is described to solve fluid flow problems numerically. It demonstrates vote prediction and dynamic results visualization based on the estimated influence matrix. Future work could explore modeling stubborn agents' topic-dependent beliefs instead of independently.
This document outlines the aim, objectives, scope, and structure of a dissertation on using genetic programming to optimize and combine K nearest neighbor classifiers for intrusion detection. The aim is to use genetic programming with the KDD Cup 1999 dataset to develop a numeric classifier that shows improved performance over individual KNN classifiers. The objectives are to determine if a GP-based numeric classifier outperforms individual KNN classifiers, if GP combination techniques produce higher performance than KNN component classifiers, and if heterogeneous KNN classifier combination performs better than homogeneous combination. The document describes the methodology that will be used, including developing an optimal KNN classifier using fitness evaluation in the first phase and combining optimal KNN classifiers based on ROC curves in the second phase.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
The document discusses machine learning algorithms for predicting customer churn in a prepaid mobile network. It presents an overview of supervised and unsupervised learning techniques including support vector machines, k-nearest neighbors, neural networks, decision trees and naive Bayes. The document outlines features for a churn prediction model, describes a demo of the model using different algorithms, and evaluates the classification accuracy and churn rates.
Comparative Study of Machine Learning Algorithms for Sentiment Analysis with ...Sagar Deogirkar
Comparing the State-of-the-Art Deep Learning with Machine Learning algorithms performance on TF-IDF vector creation for Sentiment Analysis using Airline Tweeter Data Set.
The document provides information about getting fully solved MBA assignments. It details an assignment for the subject Management Information System (MIS) for MBA Semester 2. The assignment contains 6 questions relating to topics in MIS like data flow diagrams, characteristics and functions of MIS, knowledge based systems, value chain analysis, decision making systems, enterprise resource planning, and artificial intelligence. Students are advised to send their semester and specialization details to the provided email or call the given number to get fully solved assignments.
Performance Comparisons among Machine Learning Algorithms based on the Stock ...IRJET Journal
This document compares the performance of various machine learning algorithms for predicting stock market performance based on stock market data and news data. It applies algorithms like linear regression, random forest, decision tree, K-nearest neighbors, logistic regression, linear discriminant analysis, XGBoost classifier, and Gaussian naive Bayes to datasets containing stock market values, news articles, and Reddit posts. It evaluates the algorithms based on metrics like accuracy, recall, precision and F1 score. The results suggest that linear discriminant analysis achieved the best performance at predicting stock market values based on the given datasets and evaluation metrics.
Investment Portfolio Risk Manager using Machine Learning and Deep-Learning.IRJET Journal
This document discusses building a system for predicting portfolio risk using machine learning and deep learning models. It reviews several related works that use techniques like decision trees, genetic algorithms, fuzzy logic, neural networks and sentiment analysis to predict stock performance from historical data and news/social media. The proposed work aims to take various inputs like news, tweets, foreign institutional investor data and historical stock prices to train models that can provide insights on portfolio risk and how stocks may perform. It will use long short-term memory networks with sentiment analysis and compare portfolios and historical data to make accurate predictions.
MACHINE LEARNING CLASSIFIERS TO ANALYZE CREDIT RISKIRJET Journal
This document discusses using machine learning classifiers to analyze credit risk. It examines various machine learning techniques for credit risk analysis, including Bayesian classifiers, naive Bayes, decision trees, k-nearest neighbors, multilayer perceptrons, support vector machines, and ensemble methods like bagging and boosting. Two credit datasets from the UCI machine learning repository were used to test the accuracy of these classifiers. The results showed decision trees had the highest accuracy at 89.9% and 71.25% on the two datasets, while k-nearest neighbors had the lowest. Future work could involve rebuilding the models with more accurate data to improve performance. The objective of credit risk analysis is to help banks and financial institutions balance approving loans to creditworthy borrowers
IRJET- Stock Market Forecasting Techniques: A SurveyIRJET Journal
This document surveys various techniques for stock market forecasting, including traditional and recent methods using machine learning and artificial intelligence. It discusses techniques like artificial neural networks, hidden Markov models, support vector regression, and deep learning. It also reviews several research papers that have applied methods like ARIMA models, improved Levenberg-Marquardt training for neural networks, feedforward neural networks for the Stock Exchange of Thailand index, improved multiple linear regression in an Android app, support vector regression with windowing operators on the Dhaka Stock Exchange, hidden Markov models compared to neural networks and support vector machines, a hybrid support vector regression and filtering model, and using J48 decision trees and random forests with preprocessing.
A Comparative Study on Identical Face Classification using Machine LearningIRJET Journal
This document presents research on classifying identical faces using machine learning techniques like support vector machines (SVM). The researchers aim to develop an accurate technique for identifying the same faces from facial photographs. They discuss using SVM classifiers and combining multiple SVM classifiers using plurality voting. They compare the SVM classification approach to standard identical face classification methods. The document also provides background on machine learning and supervised learning techniques like logistic regression, SVM, and random forest classifiers. It discusses related work applying SVM, neural networks, and other methods to tasks like facial expression classification, emotion classification, age and gender recognition.
Visualizing and Forecasting Stocks Using Machine LearningIRJET Journal
This document discusses using machine learning techniques like regression and LSTM models to predict stock market returns. It first provides background on the challenges of predicting the stock market due to its unpredictable nature. It then describes obtaining stock price data from Yahoo Finance to use as the dataset. The document outlines using regression analysis to build a relationship between stock prices and time and using LSTM due to its ability to learn from sequence data. It then reviews related work applying machine learning like neural networks and genetic algorithms to optimize stock prediction. The methodology section provides more detail on preprocessing the dataset and using regression and LSTM models to make predictions and compare results.
STOCK PRICE PREDICTION USING ML TECHNIQUESIRJET Journal
This document discusses using machine learning techniques like LMS and LSTM algorithms to predict stock prices. It summarizes previous research on stock price prediction that used techniques like artificial neural networks, support vector machines, and recurrent neural networks. The document then describes the proposed system for stock price prediction, which involves preprocessing data, splitting it into training and test sets, analyzing the data with LMS and LSTM algorithms, and outputting predictions in graph and report formats. It concludes that combining multiple algorithms into hybrid models can improve prediction accuracy while reducing computational complexity compared to single models.
This document describes a student performance predictor application that uses machine learning algorithms and a graphical user interface. The application predicts student performance based on academic and other details and analyzes factors that affect performance. It implements logistic regression and evaluates algorithms like support vector machine, naive bayes, and k-neighbors classifier. The application helps students and teachers by identifying strengths/weaknesses and enhancing future performance. It provides visualizations of input data and model accuracy in plots and charts through the user-friendly interface.
IRJET - Stock Market Prediction using Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms to predict stock market prices. Specifically, it analyzes using Support Vector Machine (SVM) and linear regression (LR) algorithms to predict stock prices. It finds that linear regression provides more accurate predictions than SVM when tested on the same stock data. The methodology trains models on historical stock data using these algorithms and predicts future prices, achieving up to 98% accuracy when testing linear regression predictions on Google stock prices. It concludes that input data and machine learning techniques can effectively predict stock market movements.
STOCK MARKET ANALYZING AND PREDICTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses predicting stock market movements using machine learning techniques. It begins by reviewing previous research on fundamental analysis, technical analysis and applying machine learning to stock prediction. It then proposes a methodology using machine learning algorithms like support vector machine, decision trees and classification to analyze stock market data, extract features, segment data and build a mathematical model to forecast stock prices. The goal is to help investors make better decisions by predicting stock behavior.
IRJET - House Price Prediction using Machine Learning and RPAIRJET Journal
This document discusses using machine learning and robotic process automation (RPA) to predict house prices. Specifically, it proposes using the CatBoost algorithm and RPA to extract real-time data for house price prediction. RPA involves using software robots to automate data extraction, while CatBoost will be used to predict prices based on the extracted dataset. The system aims to reduce problems faced by customers by providing more accurate price predictions compared to relying solely on real estate agents. It will extract data using RPA, clean the data, then apply machine learning algorithms like CatBoost to predict house prices based on various attributes.
This document summarizes a research paper that applied deep learning techniques like LSTM to predict stock market values. The researchers collected daily stock index data for 3 major Indian companies over 6 years. They applied machine learning models like regression, classification with Bagging, Boosting, Random Forest, ANN to the data. LSTM models were also built and showed higher accuracy than machine learning models. LSTM performed best when provided more training data. The researchers concluded that deep learning methods like LSTM can efficiently predict stock values and outperform traditional machine learning models.
Business Analysis using Machine LearningIRJET Journal
The document discusses using machine learning techniques like linear regression, random forest, and decision trees to analyze transaction data from a confectionery business in order to forecast product demand and sales. It applies these machine learning algorithms to a dataset containing over 20,000 transactions to analyze factors like product sales over time. The results can help the business optimize product offerings based on demand and improve profitability.
StocKuku - AI-Enabled Mindfulness for Profitable Stock TradingIRJET Journal
This document describes a stock trading chatbot called StocKuku that uses machine learning algorithms and natural language processing to provide stock price predictions and recommendations to users. It collects stock market data through web scraping and uses linear regression, random forest regression, and XGBoost models to predict price movements. The chatbot allows users to get personalized predictions and insights through conversational interactions. Evaluation shows the models achieved over 90% accuracy and users found the chatbot helpful. Further integration of sentiment analysis and voice assistants is discussed to improve the system.
IRJET- Financial Analysis using Data MiningIRJET Journal
The document discusses using three machine learning algorithms - K-nearest neighbors (KNN), rule-based classification, and deep learning - to predict if the NASDAQ stock market will increase in a given month. KNN achieved an accuracy of 71.28%, rule-based classification achieved 74.49% accuracy, and deep learning achieved the highest accuracy of 76.03%. Therefore, the document concludes deep learning is best suited for this stock market prediction task.
Stock Market Prediction using Long Short-Term MemoryIRJET Journal
This document discusses using a Long Short-Term Memory (LSTM) model to predict stock market prices. It begins by introducing the problem of predicting stock markets and how machine learning techniques like LSTM can help. It then discusses collecting stock price data and designing an LSTM model in Python using Keras and other libraries. The model is trained on historical stock price data to identify patterns and predict future prices. The document suggests LSTM models are well-suited for this due to their ability to use past data in predictions. It evaluates the model's predictions against actual prices to determine accuracy.
MUTUAL FUND RECOMMENDATION SYSTEM WITH PERSONALIZED EXPLANATIONSIRJET Journal
This document summarizes a research paper that proposes a mutual fund recommendation system that provides personalized explanations for its recommendations. It begins with an introduction that describes the need for such a system given the challenges that investors face in choosing appropriate mutual funds. It then reviews existing recommendation models and discusses challenges such as lack of data and cold start problems. The literature survey summarizes several papers on topics like knowledge graph-based recommendation systems, personalized equity recommendation using transfer learning, using time-series models like Prophet for predicting fund prices, network analysis approaches for portfolio recommendations, and using machine learning like deep learning for stock market and mutual fund predictions. The overall goal is to develop a model that can provide personalized mutual fund recommendations along with explanations.
The document proposes a recruiter recommendation system for undergraduate students to improve college placement processes. It uses machine learning algorithms like logistic regression, random forest, KNN and SVM to analyze previous student data and predict placement probabilities based on marks. This would help students strengthen their skills and recommend eligible companies. The system architecture involves collecting student data like CGPA and technical test scores, training models, and generating recommendations to match students with appropriate recruiters. This automated process aims to make placements more efficient by reducing manual work and better notifying students.
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
CASE STUDY: ADMISSION PREDICTION IN ENGINEERING AND TECHNOLOGY COLLEGESIRJET Journal
This document discusses a case study on using machine learning models to predict student admission to engineering colleges based on academic performance and exam ranks. It explores using linear regression, KNN regression, decision tree regression, and random forest regression models. The models are trained on data collected on student 10th grade marks, 12th grade marks, division, All India Engineering Entrance Exam (AIEEE) rank, and college ranks. Feature selection identifies the key predictive features as academic marks and exam rank. The models are evaluated to select the best performing algorithm to deploy in an application to help students predict their admission chances.
Similar to IRJET - Ensembling Reinforcement Learning for Portfolio Management (20)
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.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
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.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.