This document discusses techniques for sentiment analysis on tweets using machine learning. It examines using a lexicon-based approach with SentiWordNet to identify the sentiment polarity of tweets as positive, negative, or neutral. The document outlines collecting Twitter data using APIs, preprocessing the data by removing hashtags and part-of-speech tagging. It then proposes a model using features extracted from SentiWordNet to classify tweet sentiment and discusses experimenting with unigrams and bigrams as features for the machine learning algorithms.
SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA USING DEEP LEARNINGIRJET Journal
This document describes a system for performing sentiment analysis on social media data using deep learning techniques. The system uses entity recognition and sentiment analysis to automatically generate random variables and rules for a Bayesian network model. The model is trained using Twitter data to determine the likelihood that a user will visit a location based on their tweets. The system achieved 93% accuracy in classifying tweets as positive or negative sentiment towards different locations. The authors propose that this approach could be adapted to analyze sentiment across different social media platforms and topics.
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This document summarizes a research project that aims to analyze fake rankings on the social media platform Twitter. The project uses semi-supervised learning techniques to assign credibility scores to Twitter posts based on factors like user comments, likes, and whether the content is image, video or text. Two algorithms, LDRI and word segmentation, are used to analyze language and classify content as credible or not credible. The proposed system is intended to help prevent the spread of fake or malicious information on social media by flagging questionable content.
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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.
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This document discusses predicting social emotions from users on e-commerce platforms. It proposes developing an analytic system using prediction analysis of social emotions expressed by users in reviews and comments on e-commerce sites. The system would construct a real-time social opinion network based on semantic distances between words to predict emotions. This could help e-commerce sites improve business intelligence and decision making by analyzing customer feedback and predicting their emotions. Existing research on social emotion prediction is also discussed, including knowledge-based, statistical, and hybrid approaches.
This document proposes developing a system that collects tweets from Twitter, determines if they are positive, negative, or neutral, and suggests the best tweet to post. It aims to analyze tweet sentiment on various topics like products, people, and events. The system would first categorize tweets as positive, negative, or neutral, then use a bot to advise on the potential impact of a tweet before posting it. This could help avoid unintentionally upsetting others based on their opinions. It also discusses challenges like accurately analyzing various language styles and the costs associated with social media APIs.
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.
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.
SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA USING DEEP LEARNINGIRJET Journal
This document describes a system for performing sentiment analysis on social media data using deep learning techniques. The system uses entity recognition and sentiment analysis to automatically generate random variables and rules for a Bayesian network model. The model is trained using Twitter data to determine the likelihood that a user will visit a location based on their tweets. The system achieved 93% accuracy in classifying tweets as positive or negative sentiment towards different locations. The authors propose that this approach could be adapted to analyze sentiment across different social media platforms and topics.
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This document summarizes a research project that aims to analyze fake rankings on the social media platform Twitter. The project uses semi-supervised learning techniques to assign credibility scores to Twitter posts based on factors like user comments, likes, and whether the content is image, video or text. Two algorithms, LDRI and word segmentation, are used to analyze language and classify content as credible or not credible. The proposed system is intended to help prevent the spread of fake or malicious information on social media by flagging questionable content.
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This document discusses improving real-time Twitter sentiment analysis using machine learning and Word2Vec. It begins by introducing sentiment analysis and its importance for product analysis and business. Next, it describes extracting Twitter data using APIs, preprocessing it, and applying machine learning algorithms like Naive Bayes, logistic regression, and decision trees to classify tweets as expressing positive, negative or neutral sentiment. It also reviews literature on using techniques like linguistic analysis and ensemble models to improve sentiment analysis from social media sources.
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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.
IRJET- Analytic System Based on Prediction Analysis of Social Emotions from U...IRJET Journal
This document discusses predicting social emotions from users on e-commerce platforms. It proposes developing an analytic system using prediction analysis of social emotions expressed by users in reviews and comments on e-commerce sites. The system would construct a real-time social opinion network based on semantic distances between words to predict emotions. This could help e-commerce sites improve business intelligence and decision making by analyzing customer feedback and predicting their emotions. Existing research on social emotion prediction is also discussed, including knowledge-based, statistical, and hybrid approaches.
This document proposes developing a system that collects tweets from Twitter, determines if they are positive, negative, or neutral, and suggests the best tweet to post. It aims to analyze tweet sentiment on various topics like products, people, and events. The system would first categorize tweets as positive, negative, or neutral, then use a bot to advise on the potential impact of a tweet before posting it. This could help avoid unintentionally upsetting others based on their opinions. It also discusses challenges like accurately analyzing various language styles and the costs associated with social media APIs.
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.
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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.
This document summarizes a research paper on sentiment analysis of tweets from Twitter. It discusses how tweets are collected and preprocessed, including removing punctuation and stop words. A Naive Bayes classifier is used to classify the preprocessed tweets as positive, negative, or neutral based on a lexicon dictionary. The results are evaluated to check accuracy. Future work proposed includes computing an overall sentiment score for topics and creating a web app for users to input keywords to analyze sentiment.
IRJET- Effective Countering of Communal Hatred During Disaster Events in Soci...IRJET Journal
This document summarizes a research paper that aims to effectively counter communal hatred during disaster events on social media. It uses machine learning techniques to analyze tweets and classify them based on parameters like offensive, hatred, or neither. Tweets are collected using Twitter's API and preprocessed. A supervised machine learning algorithm (Support Vector Machine) is trained on manually labeled tweet data to classify new tweets. The results are visualized in a pie chart graph displaying the percentage of tweets containing offensive, hatred, or neutral words. The goal is to reduce the spread of communal hate speech on social media during disasters.
Implementation of Sentimental Analysis of Social Media for Stock Prediction ...IRJET Journal
This document describes a framework for predicting future stock prices based on sentiment analysis of social media data from Twitter. The framework collects tweets related to Apple Inc. over 3 months, performs sentiment analysis to classify tweets as positive or negative, and uses an ARIMA model to predict stock prices based on the sentiment values and past stock price data. The results show that predictions using tweets containing the stock symbol were more accurate than those using just the company name. Factors like the training data, preprocessing techniques, and number of tweets per time period can impact prediction accuracy. While limitations remain, the analysis demonstrates a relationship between social media sentiment and stock market movements.
UTILIZING TWITTER TO PERFORM AUTONOMOUS SENTIMENT ANALYSISIRJET Journal
This document discusses utilizing Twitter data to perform sentiment analysis. It describes collecting tweets using the Twitter API and preprocessing the data. It then explores different machine learning algorithms for sentiment classification, including Naive Bayes, Maximum Entropy, and Support Vector Machines. The results show that Naive Bayes with Laplace smoothing and SVM performed best at classifying tweet sentiment when using unigrams as features. Part-of-speech features also yielded comparable results to n-grams. Overall, the study aims to evaluate different feature combinations and machine learning algorithms for automated sentiment analysis of tweets.
IRJET- Reality Show Analytics for TRP Ratings Based on Viewer’s OpinionIRJET Journal
This document analyzes the popularity of two Indian reality TV shows, Comedy Nights with Kapil and Jabardasth, based on viewers' comments on social media. Sentiment analysis is performed on tweets and YouTube comments about the shows to calculate positive, negative, and overall scores. Various analyses are done including day-wise, week-wise, and overall to compare the popularity and predict which show is more successful based on social media feedback. Results are visualized using graphs, histograms, word clouds, and other techniques to analyze factors affecting a show's popularity. The analyses show that Comedy Nights with Kapil receives more positive sentiment than Jabardasth.
IRJET - Implementation of Twitter Sentimental Analysis According to Hash TagIRJET Journal
This document proposes a model for analyzing sentiment from tweets using hashtags. It involves collecting tweets, preprocessing the data by removing URLs and stopwords, training a classifier using Naive Bayes, and classifying tweets as positive, negative, or neutral. Hashtags are also classified to help organize tweets by topic. The proposed system is intended to help large companies understand public sentiment about their brands by analyzing tweets in real-time.
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.
Analysis and Prediction of Sentiments for Cricket Tweets using HadoopIRJET Journal
This document describes a study that analyzed and predicted sentiments for cricket tweets using Hadoop. The researchers collected tweets related to cricket matches from Twitter and analyzed them using an unsupervised machine learning method to predict the winning and losing teams based on the polarity (positive or negative sentiment) of the tweets. Hadoop was used as it can process large volumes of unstructured social media data in a distributed manner. The study aims to develop an intelligent system to accurately analyze sentiments from cricket tweets and predict match outcomes prior to games being played.
This document describes a web application that analyzes trends on Twitter. The application uses latent Dirichlet allocation and clustering algorithms to categorize tweets by topic, location, and time. It allows users to search for trending events and view results graphically. The application was developed using an iterative software development process and addresses the problem of users not easily being able to find out about trending events. It provides a convenient way for users to learn about events through a simple interface without needing other media. Security is ensured through two-factor authentication. The application responds quickly and uses LDA for efficient clustering so users can access trending tweets from any location. Sentiment analysis is also performed by clustering positive and negative tweets.
IRJET- Sentimental Analysis of Twitter Data for Job OpportunitiesIRJET Journal
This document discusses sentiment analysis of tweets related to job opportunities. It begins with an introduction to sentiment analysis and its applications. It then discusses how Twitter is a rich source of data for sentiment analysis due to the large number of daily posts, but that analyzing sentiment in tweets is challenging due to their short length and use of abbreviations. The document then outlines the design and implementation of the sentiment analysis, which involves downloading tweets and sentiment dictionaries, cleaning the tweet data by removing stop words and tokenizing, comparing words to dictionaries to determine sentiment scores, and classifying tweets as positive, negative or neutral based on the scores.
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.
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.
IRJET- Sentiment Analysis of Twitter Data using PythonIRJET Journal
1. The document discusses sentiment analysis of tweets using Python. It involves collecting tweet data using Twitter API and classifying the tweets as positive or negative using machine learning algorithms in Python.
2. It proposes using Anaconda Python to analyze tweets collected from Twitter and extract features from tweets like unigrams and bigrams to represent the tweet text. Various machine learning algorithms will then be used to conduct sentiment analysis and classify tweets as positive or negative.
3. The accuracy of individual models is limited, so the approach is to use the predictions from the top models to generate an ensemble model for improved accuracy of sentiment classification of tweets.
This document describes a social media content analysis project that analyzes data from platforms like Facebook, Instagram, Twitter, and YouTube. The project is a web application that allows business users, influencers, and personal users to view analytics on their social media profiles. It features post scheduling, campaign generation, sentiment analysis of comments, and hashtag suggestion. The system architecture includes modules for analyzing metrics like followers/likes, post scheduling, campaign generation, sentiment analysis using Naive Bayes or SVM, and hashtag suggestion using keywords from images. The project aims to help users monitor their social media growth and enhance their profiles through automated posting and campaign tools.
This document discusses Twitter sentiment analysis and describes the creation of a sentiment analysis program to classify tweets as positive or negative. Key points:
- The program extracts tweets on a given keyword, preprocesses the tweets, uses a machine learning model trained on tweet sentiment to predict sentiment, and displays results in a graph.
- Sentiment analysis can help businesses gain insights from social media by classifying opinions as positive, negative, or neutral. It allows assessing customer views.
- The paper presents the design and implementation of the sentiment analysis program and discusses advantages like discovering brand perceptions, limitations like accuracy across domains, and concludes sentiment analysis on Twitter can provide useful insights into public opinions.
This document discusses Twitter sentiment analysis and describes the creation of a sentiment analysis program that classifies tweets as positive or negative. Key points:
- The program extracts a large number of tweets on a given topic, then uses machine learning classifiers like Naive Bayes to categorize the sentiment as positive or negative.
- Results are represented using a pie chart or table to give an overview of public opinion on various topics based on analysis of tweets.
- The system could help businesses gain insights from social media to improve products, services, and marketing strategies.
DETECTION OF MALICIOUS SOCIAL BOTS USING ML TECHNIQUE IN TWITTER NETWORKIRJET Journal
The document discusses a machine learning approach called LA-MSBD (Learning Automata-based Malicious Social Bot Detection) algorithm for identifying trustworthy users on Twitter by integrating a Naive Bayes algorithm model with URL-based features for classification and feature extraction. The proposed algorithm extracts features like frequency of shared URLs, DNS fluxiness, network features, link popularity from URLs to detect malicious social bots, requiring less time than methods using social graph-based features. An experiment on two Twitter datasets showed the algorithm improved precision and detection accuracy compared to existing methods.
There are various online networking sites such as Facebook, twitter where students casually discuss their educational
experiences, their opinions, emotions, and concerns about the learning process. Information from such open environment can
give valuable knowledge for opinions, emotions and help the educational organizations to get insight into students’ educational
life. Analysing down such data, on the other hand, can be challenging therefore a qualitative research and significant data
mining process needs to be done. Sentiment classification can be done using NLP (Natural Language Processing). For a social
network that provides micro blogging services such as twitter, the incoming tweets can be classified into News, Opinions,
Events, Deals and private Messages based on authors information available in the tweets. This approach is similar to
Tweetstand, which classifies the tweets into news and non-news. Even for e-commerce applications virtual customer
environments can be created using social networking sites. Since the data is ever growing, using data mining techniques can get
difficult, hence we can use data analysis tools
IRJET - Election Result Prediction using Sentiment AnalysisIRJET Journal
This document proposes a method to predict election results using sentiment analysis of social media data. It involves collecting data from Twitter, Facebook, and Instagram using their APIs. The data will then be preprocessed by removing special characters and URLs. Popular machine learning algorithms like Naive Bayes and SVM will be trained on the preprocessed data to classify tweets as positive, negative, or neutral sentiment toward political parties. The classified tweets will then be analyzed to predict the outcome of elections.
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.
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
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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.
Similar to Classification of Sentiment Analysis on Tweets Based on Techniques from Machine Learning (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.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
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.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
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.