The document summarizes research on using machine learning techniques to analyze sentiment and detect stress levels from text data on Reddit. It discusses prior work applying preprocessing methods like tokenization, stemming and stop word removal. Feature extraction techniques explored include n-grams, LIWC, LDA and TF-IDF. Supervised learning classifiers tested were SVM, logistic regression, random forest, AdaBoost and neural networks. The best performing model was logistic regression with domain-specific word embeddings, achieving an F1 score of 79.80% for stress detection.
Twitter Sentiment Analysis: An Unsupervised ApproachIRJET Journal
The document describes a study that performs sentiment analysis on Twitter data using an unsupervised machine learning technique. It discusses how Twitter data was collected and preprocessed, including removing stopwords and lemmatizing words. It then used the FastText word embedding model to represent words as vectors, which is suitable for unlabeled data. The K-Means clustering algorithm was implemented to group the Twitter data into clusters in an unsupervised manner and classify the tweets as positive, negative, or neutral sentiment.
Twitter Text Sentiment Analysis: A Comparative Study on Unigram and Bigram Fe...IRJET Journal
This document discusses sentiment analysis on Twitter text using unigram and bigram feature extraction. It compares the performance of four machine learning classifiers (Naive Bayes Multinomial, 5-NN, SMO, REPTree) on a Twitter dataset using unigram and bigram features. The results show that the 5-NN algorithm achieved the highest accuracy of 85.83% when using bigram features, outperforming the use of only unigram features. The study aims to evaluate different classifiers' performance on sentiment analysis of tweets and determine the most effective feature for gleaning sentiments from Twitter posts.
Election Result Prediction using Twitter AnalysisIRJET Journal
The document describes a study that aims to predict election results in India by analyzing tweets related to major political parties. The researchers collected over 12,000 tweets using hashtags and phrases related to upcoming state elections in 2022. They preprocessed the tweets by removing URLs, handles, punctuations, stopwords, and lemmatizing words. The tweets were then labeled as positive, negative or neutral using a sentiment analysis tool. Various machine learning algorithms like logistic regression, SVM and random forest were trained on 75% of the labeled data and tested on the remaining 25% to classify tweets and predict election outcomes. The goal is to help the public and political parties understand voter sentiment from Twitter to aid in predicting election results.
This document summarizes research on sentiment analysis of Twitter data. It discusses how sentiment analysis can classify tweets as positive, negative, or neutral. It reviews different techniques for sentiment analysis, including machine learning approaches like Naive Bayes classifiers and lexicon-based approaches. The document also describes prior studies that have used sentiment analysis techniques to predict security attacks based on Twitter sentiment and explore improvements in classification accuracy. In general, the document outlines common methods for analyzing sentiment in social media data and highlights past applications of the analysis.
IRJET- Comparative Study of Classification Algorithms for Sentiment Analy...IRJET Journal
This document provides a comparative study of classification algorithms for sentiment analysis on Twitter data. It discusses Naive Bayes, Random Forest and Support Vector Machine (SVM) algorithms. For each algorithm, it describes the basic theory, common uses, and pros and cons. It also outlines the process used for sentiment analysis, including data collection from Twitter, preprocessing, feature extraction and classification. The goal is to evaluate which algorithm performs best for sentiment classification of tweets.
Emotion Recognition By Textual Tweets Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to perform sentiment analysis on tweets in order to predict election results in India. It begins with an introduction to sentiment analysis and how it can be applied to social media tweets. It then discusses existing methods for sentiment analysis that have certain disadvantages. The proposed system aims to improve accuracy by using techniques like Naive Bayes classification, support vector machines, decision trees, and long short-term memory networks. It presents the system design, implementation details using Python and various machine learning algorithms, and testing of the system to classify tweets by emotion and predict election outcomes.
Review on Sentiment Analysis on Customer ReviewsIRJET Journal
This document discusses sentiment analysis on customer reviews using natural language processing and machine learning algorithms. It summarizes the process of preprocessing text data through techniques like tokenization and stemming before classifying the sentiment using algorithms like Naive Bayes, support vector machines, and logistic regression. The highest accuracy was obtained from logistic regression. The goal is to help businesses analyze customer sentiments from reviews to improve their products and services.
Combining Lexicon based and Machine Learning based Methods for Twitter Sentim...IRJET Journal
This document discusses combining lexicon-based and machine learning methods for Twitter sentiment analysis. It first describes lexicon-based approaches like TextBlob and Vader that use sentiment lexicons to determine tweet polarity. It then discusses machine learning approaches like random forest, support vector machines, and decision trees that are trained on labeled tweet data. The document finds that a random forest classifier achieved the highest accuracy of 99.92% at predicting tweet sentiment, demonstrating the effectiveness of combining both lexicon-based and machine learning methods for Twitter sentiment analysis.
Twitter Sentiment Analysis: An Unsupervised ApproachIRJET Journal
The document describes a study that performs sentiment analysis on Twitter data using an unsupervised machine learning technique. It discusses how Twitter data was collected and preprocessed, including removing stopwords and lemmatizing words. It then used the FastText word embedding model to represent words as vectors, which is suitable for unlabeled data. The K-Means clustering algorithm was implemented to group the Twitter data into clusters in an unsupervised manner and classify the tweets as positive, negative, or neutral sentiment.
Twitter Text Sentiment Analysis: A Comparative Study on Unigram and Bigram Fe...IRJET Journal
This document discusses sentiment analysis on Twitter text using unigram and bigram feature extraction. It compares the performance of four machine learning classifiers (Naive Bayes Multinomial, 5-NN, SMO, REPTree) on a Twitter dataset using unigram and bigram features. The results show that the 5-NN algorithm achieved the highest accuracy of 85.83% when using bigram features, outperforming the use of only unigram features. The study aims to evaluate different classifiers' performance on sentiment analysis of tweets and determine the most effective feature for gleaning sentiments from Twitter posts.
Election Result Prediction using Twitter AnalysisIRJET Journal
The document describes a study that aims to predict election results in India by analyzing tweets related to major political parties. The researchers collected over 12,000 tweets using hashtags and phrases related to upcoming state elections in 2022. They preprocessed the tweets by removing URLs, handles, punctuations, stopwords, and lemmatizing words. The tweets were then labeled as positive, negative or neutral using a sentiment analysis tool. Various machine learning algorithms like logistic regression, SVM and random forest were trained on 75% of the labeled data and tested on the remaining 25% to classify tweets and predict election outcomes. The goal is to help the public and political parties understand voter sentiment from Twitter to aid in predicting election results.
This document summarizes research on sentiment analysis of Twitter data. It discusses how sentiment analysis can classify tweets as positive, negative, or neutral. It reviews different techniques for sentiment analysis, including machine learning approaches like Naive Bayes classifiers and lexicon-based approaches. The document also describes prior studies that have used sentiment analysis techniques to predict security attacks based on Twitter sentiment and explore improvements in classification accuracy. In general, the document outlines common methods for analyzing sentiment in social media data and highlights past applications of the analysis.
IRJET- Comparative Study of Classification Algorithms for Sentiment Analy...IRJET Journal
This document provides a comparative study of classification algorithms for sentiment analysis on Twitter data. It discusses Naive Bayes, Random Forest and Support Vector Machine (SVM) algorithms. For each algorithm, it describes the basic theory, common uses, and pros and cons. It also outlines the process used for sentiment analysis, including data collection from Twitter, preprocessing, feature extraction and classification. The goal is to evaluate which algorithm performs best for sentiment classification of tweets.
Emotion Recognition By Textual Tweets Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to perform sentiment analysis on tweets in order to predict election results in India. It begins with an introduction to sentiment analysis and how it can be applied to social media tweets. It then discusses existing methods for sentiment analysis that have certain disadvantages. The proposed system aims to improve accuracy by using techniques like Naive Bayes classification, support vector machines, decision trees, and long short-term memory networks. It presents the system design, implementation details using Python and various machine learning algorithms, and testing of the system to classify tweets by emotion and predict election outcomes.
Review on Sentiment Analysis on Customer ReviewsIRJET Journal
This document discusses sentiment analysis on customer reviews using natural language processing and machine learning algorithms. It summarizes the process of preprocessing text data through techniques like tokenization and stemming before classifying the sentiment using algorithms like Naive Bayes, support vector machines, and logistic regression. The highest accuracy was obtained from logistic regression. The goal is to help businesses analyze customer sentiments from reviews to improve their products and services.
Combining Lexicon based and Machine Learning based Methods for Twitter Sentim...IRJET Journal
This document discusses combining lexicon-based and machine learning methods for Twitter sentiment analysis. It first describes lexicon-based approaches like TextBlob and Vader that use sentiment lexicons to determine tweet polarity. It then discusses machine learning approaches like random forest, support vector machines, and decision trees that are trained on labeled tweet data. The document finds that a random forest classifier achieved the highest accuracy of 99.92% at predicting tweet sentiment, demonstrating the effectiveness of combining both lexicon-based and machine learning methods for Twitter sentiment analysis.
IRJET - Sentiment Analysis of Posts and Comments of OSNIRJET Journal
This document summarizes a research paper that aims to perform sentiment analysis on posts and comments on online social networks like Twitter. The proposed system seeks to identify the sentiment behind content posted to determine if users exhibit signs of depression. It will analyze text for positive emotions like happy and negative emotions like sad using machine learning techniques. The results will then classify the degree of negative sentiment and potential depression displayed by the user. The system architecture involves collecting social media data, filtering out noise, comparing text to stored emotional words, and generating a result that calculates sentiment scores and ranks emotions displayed in the content.
A STUDY ON TWITTER SENTIMENT ANALYSIS USING DEEP LEARNINGIRJET Journal
This document discusses a study on sentiment analysis of tweets using deep learning techniques. The study aims to classify tweets from the SemEval-2017 Twitter dataset as either positive or negative sentiment using various deep learning models including bidirectional LSTM, CNNs, and BERT. The models are trained on the dataset and evaluated to determine the most accurate classifier. Preprocessing of the tweets is also discussed, which includes normalization, removal of URLs, usernames, hashtags, and special characters.
Hybrid Classifier for Sentiment Analysis using Effective PipeliningIRJET Journal
The document describes a hybrid approach for sentiment analysis of tweets that uses a pipeline of rules-based classification, lexicon-based classification, and machine learning classification. Tweets are first classified using rules and a lexicon, and only tweets that do not meet a confidence threshold are passed to a machine learning classifier. The hybrid approach aims to optimize performance, speed, accuracy, and processing requirements compared to using individual classification methods alone. The document provides background on sentiment analysis methods and evaluates the performance of the hybrid approach versus individual classifiers.
A Comparative Study of different Classifiers on Political Data for Classifica...IRJET Journal
This document presents a comparative study of different machine learning classifiers for classifying political sentiment in Hinglish text comments from YouTube videos. The study aims to determine the most promising classifier for identifying sentiment (positive, negative, neutral) in Hinglish comments to provide useful data for political parties. The proposed methodology involves data collection, preprocessing, representation using n-grams and TF-IDF, training models like multilayer perceptron and convolutional neural networks on the data, and evaluating performance. The document reviews related work applying deep learning to sentiment analysis and discusses preprocessing steps like removing stopwords, tokenization, and data splitting. The goal is to help political groups with campaigning by understanding voter sentiment from social media comments.
IRJET- Analysis of Brand Value Prediction based on Social Media DataIRJET Journal
This document presents a study that analyzes brand value prediction based on social media data using different sentiment analysis techniques. The study compares lexicon-based sentiment analysis tools SentiWordNet and TextBlob, and also evaluates supervised machine learning classifiers Naive Bayes and CNN. The CNN model achieved the highest accuracy of 94.4% when applied to a dataset of Amazon product reviews, outperforming the Naive Bayes model which achieved 82% accuracy. The study concludes that hybrid methods combining lexicon-based and machine learning approaches can effectively analyze sentiment from large social media datasets.
Sentiment Analysis on Product Reviews Using Supervised Learning TechniquesIRJET Journal
This document discusses sentiment analysis on product reviews using supervised machine learning techniques. It compares three classifiers - Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. The paper extracts features from product reviews datasets using bag-of-words, TF-IDF, and removes irrelevant nouns. It then splits the datasets into training and test sets. The classifiers are trained on the training set and evaluated on the test set based on precision, recall, F1-score and accuracy. Confusion matrices are used to evaluate performance. SVM had the best performance on the Alexa dataset, while SVM performed best on the mobile phone dataset.
Development of Information Extraction for Data Analysis using NLPIRJET Journal
This document describes a proposed algorithm for extracting information from PDF documents using natural language processing (NLP). The algorithm aims to automate the extraction of key data like company metrics and financial details that analysts currently extract manually. It involves identifying keywords, extracting text and tables using rule-based filters, and presenting the extracted information in a structured format like a table. The algorithm is intended to simplify the information extraction process and make it scalable for large documents. It provides a framework that can be modified based on user needs and categories of interest.
This document summarizes research on analyzing sentiment from Twitter data using machine learning techniques. The researchers collected Twitter data using the Tweepy API and analyzed sentiment using algorithms like SGD, LGR, MNB, and random forest classification. They found that MNB had the highest accuracy at 62% for determining whether tweets expressed positive, negative, or neutral sentiment. The study provides a comparison of different machine learning methods for Twitter sentiment analysis and classification.
IRJET- The Sentimental Analysis on Product Reviews of Amazon Data using the H...IRJET Journal
This document summarizes a research paper that analyzes sentiment on product reviews from Amazon using a hybrid approach. The researchers collected a dataset from the Amazon API and performed preprocessing including stemming, error correction, and stop word removal. They used n-gram analysis to extract features and defined positive, negative, and neutral words. SentiWordNet was used to determine sentiment polarities. A k-nearest neighbor classifier called WDE-KNN was trained on the dataset and used to classify sentiments into positive, negative or neutral. The researchers conducted experiments using different training-testing splits and found that KNN achieved higher accuracy than SVM, with up to 85.32% accuracy when the training and testing data was split 50-50.
Estimating the Efficacy of Efficient Machine Learning Classifiers for Twitter...IRJET Journal
This document summarizes research estimating the efficacy of machine learning classifiers for Twitter sentiment analysis. It explores using decision trees, random forest, support vector machine, Naive Bayes, logistic regression, and XGBoost classifiers on a dataset of 1.6 million tweets. The classifiers are evaluated based on accuracy to determine the most appropriate model for Twitter sentiment analysis classification.
Predicting Employee Attrition using various techniques of Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict employee attrition. It begins with an introduction stating that attrition can negatively impact businesses by requiring rehiring and training of replacement employees. It then reviews related literature on factors that influence attrition like work-life balance and career opportunities.
The document describes the design of predicting attrition using various machine learning algorithms on an employee dataset. It tests algorithms like logistic regression, decision trees, KNN, SVM, random forest and naive bayes. Evaluation shows logistic regression had the highest accuracy at predicting attrition at 87.7%, followed by random forest at 83.2%.
IRJET- User Behavior Analysis on Social Media Data using Sentiment Analysis o...IRJET Journal
This document discusses user behavior analysis on social media data using sentiment analysis. It describes extracting data from social media platforms like Twitter using hashtags and keywords. The data is then preprocessed by removing URLs, symbols, stop words etc. Features like sentiment, unigrams, emoticons are extracted and classification algorithms like Naive Bayes, K-NN, Random Forest are used to classify the data based on sentiment. The results can help understand user behavior and opinions on various topics from their social media posts and comments.
Sentiment Analysis and Classification of Tweets using Data MiningIRJET Journal
This document summarizes research on using data mining techniques to perform sentiment analysis on tweets. The researchers collected tweets from Twitter and preprocessed the text to make it usable for building sentiment classifiers. They used three classifiers - K-Nearest Neighbor, Naive Bayes, and Decision Tree - and compared the results to determine which provided the best accuracy. Rapid Miner tool was used to preprocess the text, build the classifiers, and analyze the results. The goal was to determine people's sentiments expressed in their tweets and correctly classify them.
This document presents a framework for automatically ranking the important aspects of products from online consumer reviews. It identifies product aspects from reviews using a shallow dependency parser and determines consumer sentiment on each aspect using a classifier. It then develops a probabilistic algorithm to infer the importance of each aspect based on how frequently it is mentioned and how consumer sentiment towards that aspect influences their overall product opinion. The approach is tested on a corpus of reviews for 21 popular products across 8 domains and is shown to effectively rank product aspects and improve performance on sentiment classification and review summarization tasks.
Sentiment Analysis on Twitter data using Machine LearningIRJET Journal
This document discusses sentiment analysis on Twitter data using machine learning. It describes how Twitter is used to express opinions and sentiments, and how sentiment analysis can be used to extract user sentiments from tweets. The document outlines how Python libraries like TextBlob and Tweepy can be used to connect to the Twitter API, retrieve tweets, clean the data, perform sentiment analysis using a sentiment dictionary, and visualize the results. It presents the results of analyzing tweets about a particular topic, finding most tweets had a neutral sentiment, with more negative than positive. The conclusion discusses the improvements in models over time but remaining challenges from data variety and informal language on Twitter.
To Analyze Conflicts between Software Developer and Software TesterAM Publications,India
This document analyzes conflicts between software developers and testers based on responses from 19 companies in different domains. It finds that while most companies aim for a balanced ratio of developers to testers, the optimal ratio depends on factors like the project stage and type. Conflicts sometimes arise due to misunderstandings or different priorities between developers focused on efficiency and testers focused on quality. However, companies resolve conflicts through clear communication, understanding different perspectives, and ensuring all roles are well-defined. While conflicts can impact goals if not managed, most companies feel other factors like motivation have a larger influence, and effective project management can mitigate issues.
IRJET- A Comparative Research of Rule based Classification on Dataset using W...IRJET Journal
This document discusses and compares the performance of four rule-based classification algorithms (Decision Table, One R, PART, and Zero R) on different datasets using the WEKA data mining tool. It first provides background on classification and rule-based classification in data mining. It then describes the four algorithms and the experimental process used to implement them in WEKA, evaluate their performance based on accuracy, number of correct/incorrect predictions, and execution time, and analyze the results.
Abstract
Big data plays a serious role within the business for creating higher predictions over business information that is collected from the real world. Finance is that the new sector wherever the big data technologies like Hadoop, NoSQL are creating its mark in predictions from financial data by the analysts. It’s a lot of fascinating within the stock exchange choices which might predict on a lot of profits of stock exchange. For this stock exchange analysis each regular information and historical information of specific stock exchange are needed for creating predictions. There are varied techniques used for analyzing the unstructured information like stock exchange reviews (day-to-day information) and historical statistic of economic information severally. This paper involves discussion regarding the strategies that square measure used for analyzing each varieties of information.
Keywords: Big data, prediction, finance, stock market, business intelligence
Cross Domain Recommender System using Machine Learning and Transferable Knowl...IRJET Journal
This document discusses a proposed cross-domain recommender system that uses machine learning techniques to address the cold start problem. It involves a two stage process:
1) The TrAdaBoost algorithm is used to select some initial items to recommend to users in the target domain.
2) A nonparametric pairwise clustering algorithm clusters users into groups based on whether they would recommend an item or not.
PageRank is then used to provide additional relevant and unsearched content to users. The goal is to transfer useful knowledge from an auxiliary domain to help address cold start issues in the target domain.
A Survey on Analysis of Twitter Opinion Mining using Sentiment AnalysisIRJET Journal
This document summarizes a research paper on analyzing sentiment in tweets using sentiment analysis. It discusses how social media generates large amounts of user data that can be analyzed. Sentiment analysis involves classifying opinions expressed in tweets as positive, negative or neutral. This can be done using supervised machine learning approaches or unsupervised lexicon-based approaches. The document also outlines the common steps in sentiment analysis of twitter data: data collection, preprocessing, feature extraction, classification and discusses examples of each approach. Finally, it lists some popular tools used for sentiment analysis.
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
More Related Content
Similar to Stress Sentimental Analysis Using Machine learning (Reddit): A Review
IRJET - Sentiment Analysis of Posts and Comments of OSNIRJET Journal
This document summarizes a research paper that aims to perform sentiment analysis on posts and comments on online social networks like Twitter. The proposed system seeks to identify the sentiment behind content posted to determine if users exhibit signs of depression. It will analyze text for positive emotions like happy and negative emotions like sad using machine learning techniques. The results will then classify the degree of negative sentiment and potential depression displayed by the user. The system architecture involves collecting social media data, filtering out noise, comparing text to stored emotional words, and generating a result that calculates sentiment scores and ranks emotions displayed in the content.
A STUDY ON TWITTER SENTIMENT ANALYSIS USING DEEP LEARNINGIRJET Journal
This document discusses a study on sentiment analysis of tweets using deep learning techniques. The study aims to classify tweets from the SemEval-2017 Twitter dataset as either positive or negative sentiment using various deep learning models including bidirectional LSTM, CNNs, and BERT. The models are trained on the dataset and evaluated to determine the most accurate classifier. Preprocessing of the tweets is also discussed, which includes normalization, removal of URLs, usernames, hashtags, and special characters.
Hybrid Classifier for Sentiment Analysis using Effective PipeliningIRJET Journal
The document describes a hybrid approach for sentiment analysis of tweets that uses a pipeline of rules-based classification, lexicon-based classification, and machine learning classification. Tweets are first classified using rules and a lexicon, and only tweets that do not meet a confidence threshold are passed to a machine learning classifier. The hybrid approach aims to optimize performance, speed, accuracy, and processing requirements compared to using individual classification methods alone. The document provides background on sentiment analysis methods and evaluates the performance of the hybrid approach versus individual classifiers.
A Comparative Study of different Classifiers on Political Data for Classifica...IRJET Journal
This document presents a comparative study of different machine learning classifiers for classifying political sentiment in Hinglish text comments from YouTube videos. The study aims to determine the most promising classifier for identifying sentiment (positive, negative, neutral) in Hinglish comments to provide useful data for political parties. The proposed methodology involves data collection, preprocessing, representation using n-grams and TF-IDF, training models like multilayer perceptron and convolutional neural networks on the data, and evaluating performance. The document reviews related work applying deep learning to sentiment analysis and discusses preprocessing steps like removing stopwords, tokenization, and data splitting. The goal is to help political groups with campaigning by understanding voter sentiment from social media comments.
IRJET- Analysis of Brand Value Prediction based on Social Media DataIRJET Journal
This document presents a study that analyzes brand value prediction based on social media data using different sentiment analysis techniques. The study compares lexicon-based sentiment analysis tools SentiWordNet and TextBlob, and also evaluates supervised machine learning classifiers Naive Bayes and CNN. The CNN model achieved the highest accuracy of 94.4% when applied to a dataset of Amazon product reviews, outperforming the Naive Bayes model which achieved 82% accuracy. The study concludes that hybrid methods combining lexicon-based and machine learning approaches can effectively analyze sentiment from large social media datasets.
Sentiment Analysis on Product Reviews Using Supervised Learning TechniquesIRJET Journal
This document discusses sentiment analysis on product reviews using supervised machine learning techniques. It compares three classifiers - Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. The paper extracts features from product reviews datasets using bag-of-words, TF-IDF, and removes irrelevant nouns. It then splits the datasets into training and test sets. The classifiers are trained on the training set and evaluated on the test set based on precision, recall, F1-score and accuracy. Confusion matrices are used to evaluate performance. SVM had the best performance on the Alexa dataset, while SVM performed best on the mobile phone dataset.
Development of Information Extraction for Data Analysis using NLPIRJET Journal
This document describes a proposed algorithm for extracting information from PDF documents using natural language processing (NLP). The algorithm aims to automate the extraction of key data like company metrics and financial details that analysts currently extract manually. It involves identifying keywords, extracting text and tables using rule-based filters, and presenting the extracted information in a structured format like a table. The algorithm is intended to simplify the information extraction process and make it scalable for large documents. It provides a framework that can be modified based on user needs and categories of interest.
This document summarizes research on analyzing sentiment from Twitter data using machine learning techniques. The researchers collected Twitter data using the Tweepy API and analyzed sentiment using algorithms like SGD, LGR, MNB, and random forest classification. They found that MNB had the highest accuracy at 62% for determining whether tweets expressed positive, negative, or neutral sentiment. The study provides a comparison of different machine learning methods for Twitter sentiment analysis and classification.
IRJET- The Sentimental Analysis on Product Reviews of Amazon Data using the H...IRJET Journal
This document summarizes a research paper that analyzes sentiment on product reviews from Amazon using a hybrid approach. The researchers collected a dataset from the Amazon API and performed preprocessing including stemming, error correction, and stop word removal. They used n-gram analysis to extract features and defined positive, negative, and neutral words. SentiWordNet was used to determine sentiment polarities. A k-nearest neighbor classifier called WDE-KNN was trained on the dataset and used to classify sentiments into positive, negative or neutral. The researchers conducted experiments using different training-testing splits and found that KNN achieved higher accuracy than SVM, with up to 85.32% accuracy when the training and testing data was split 50-50.
Estimating the Efficacy of Efficient Machine Learning Classifiers for Twitter...IRJET Journal
This document summarizes research estimating the efficacy of machine learning classifiers for Twitter sentiment analysis. It explores using decision trees, random forest, support vector machine, Naive Bayes, logistic regression, and XGBoost classifiers on a dataset of 1.6 million tweets. The classifiers are evaluated based on accuracy to determine the most appropriate model for Twitter sentiment analysis classification.
Predicting Employee Attrition using various techniques of Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict employee attrition. It begins with an introduction stating that attrition can negatively impact businesses by requiring rehiring and training of replacement employees. It then reviews related literature on factors that influence attrition like work-life balance and career opportunities.
The document describes the design of predicting attrition using various machine learning algorithms on an employee dataset. It tests algorithms like logistic regression, decision trees, KNN, SVM, random forest and naive bayes. Evaluation shows logistic regression had the highest accuracy at predicting attrition at 87.7%, followed by random forest at 83.2%.
IRJET- User Behavior Analysis on Social Media Data using Sentiment Analysis o...IRJET Journal
This document discusses user behavior analysis on social media data using sentiment analysis. It describes extracting data from social media platforms like Twitter using hashtags and keywords. The data is then preprocessed by removing URLs, symbols, stop words etc. Features like sentiment, unigrams, emoticons are extracted and classification algorithms like Naive Bayes, K-NN, Random Forest are used to classify the data based on sentiment. The results can help understand user behavior and opinions on various topics from their social media posts and comments.
Sentiment Analysis and Classification of Tweets using Data MiningIRJET Journal
This document summarizes research on using data mining techniques to perform sentiment analysis on tweets. The researchers collected tweets from Twitter and preprocessed the text to make it usable for building sentiment classifiers. They used three classifiers - K-Nearest Neighbor, Naive Bayes, and Decision Tree - and compared the results to determine which provided the best accuracy. Rapid Miner tool was used to preprocess the text, build the classifiers, and analyze the results. The goal was to determine people's sentiments expressed in their tweets and correctly classify them.
This document presents a framework for automatically ranking the important aspects of products from online consumer reviews. It identifies product aspects from reviews using a shallow dependency parser and determines consumer sentiment on each aspect using a classifier. It then develops a probabilistic algorithm to infer the importance of each aspect based on how frequently it is mentioned and how consumer sentiment towards that aspect influences their overall product opinion. The approach is tested on a corpus of reviews for 21 popular products across 8 domains and is shown to effectively rank product aspects and improve performance on sentiment classification and review summarization tasks.
Sentiment Analysis on Twitter data using Machine LearningIRJET Journal
This document discusses sentiment analysis on Twitter data using machine learning. It describes how Twitter is used to express opinions and sentiments, and how sentiment analysis can be used to extract user sentiments from tweets. The document outlines how Python libraries like TextBlob and Tweepy can be used to connect to the Twitter API, retrieve tweets, clean the data, perform sentiment analysis using a sentiment dictionary, and visualize the results. It presents the results of analyzing tweets about a particular topic, finding most tweets had a neutral sentiment, with more negative than positive. The conclusion discusses the improvements in models over time but remaining challenges from data variety and informal language on Twitter.
To Analyze Conflicts between Software Developer and Software TesterAM Publications,India
This document analyzes conflicts between software developers and testers based on responses from 19 companies in different domains. It finds that while most companies aim for a balanced ratio of developers to testers, the optimal ratio depends on factors like the project stage and type. Conflicts sometimes arise due to misunderstandings or different priorities between developers focused on efficiency and testers focused on quality. However, companies resolve conflicts through clear communication, understanding different perspectives, and ensuring all roles are well-defined. While conflicts can impact goals if not managed, most companies feel other factors like motivation have a larger influence, and effective project management can mitigate issues.
IRJET- A Comparative Research of Rule based Classification on Dataset using W...IRJET Journal
This document discusses and compares the performance of four rule-based classification algorithms (Decision Table, One R, PART, and Zero R) on different datasets using the WEKA data mining tool. It first provides background on classification and rule-based classification in data mining. It then describes the four algorithms and the experimental process used to implement them in WEKA, evaluate their performance based on accuracy, number of correct/incorrect predictions, and execution time, and analyze the results.
Abstract
Big data plays a serious role within the business for creating higher predictions over business information that is collected from the real world. Finance is that the new sector wherever the big data technologies like Hadoop, NoSQL are creating its mark in predictions from financial data by the analysts. It’s a lot of fascinating within the stock exchange choices which might predict on a lot of profits of stock exchange. For this stock exchange analysis each regular information and historical information of specific stock exchange are needed for creating predictions. There are varied techniques used for analyzing the unstructured information like stock exchange reviews (day-to-day information) and historical statistic of economic information severally. This paper involves discussion regarding the strategies that square measure used for analyzing each varieties of information.
Keywords: Big data, prediction, finance, stock market, business intelligence
Cross Domain Recommender System using Machine Learning and Transferable Knowl...IRJET Journal
This document discusses a proposed cross-domain recommender system that uses machine learning techniques to address the cold start problem. It involves a two stage process:
1) The TrAdaBoost algorithm is used to select some initial items to recommend to users in the target domain.
2) A nonparametric pairwise clustering algorithm clusters users into groups based on whether they would recommend an item or not.
PageRank is then used to provide additional relevant and unsearched content to users. The goal is to transfer useful knowledge from an auxiliary domain to help address cold start issues in the target domain.
A Survey on Analysis of Twitter Opinion Mining using Sentiment AnalysisIRJET Journal
This document summarizes a research paper on analyzing sentiment in tweets using sentiment analysis. It discusses how social media generates large amounts of user data that can be analyzed. Sentiment analysis involves classifying opinions expressed in tweets as positive, negative or neutral. This can be done using supervised machine learning approaches or unsupervised lexicon-based approaches. The document also outlines the common steps in sentiment analysis of twitter data: data collection, preprocessing, feature extraction, classification and discusses examples of each approach. Finally, it lists some popular tools used for sentiment analysis.
Similar to Stress Sentimental Analysis Using Machine learning (Reddit): A Review (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.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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/)
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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.
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.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.