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.
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This document proposes using Twitter sentiment analysis and an LSTM neural network to predict election results. It involves collecting tweets related to political parties and candidates in India, cleaning the data, training an LSTM classifier on labeled tweets, and using the trained model to classify tweets as positive, negative or neutral sentiment and compare sentiment levels for each candidate. The goal is to analyze public sentiment expressed on Twitter and how it correlates with actual election outcomes.
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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.
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As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
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The document presents a study that analyzes sentiment in tweets related to COVID-19 using an enhanced stacked ensemble model. It first reviews previous research on sentiment analysis of social media data during the pandemic. It then describes collecting a dataset of over 40,000 COVID-19 tweets from March-April 2020. Various classification algorithms are tested to predict sentiment, including naive bayes, random forest, XGBoost, and an enhanced stacked ensemble. The enhanced stacked ensemble achieved the highest test accuracy of 86% according to the experimental results presented.
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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 proposes using Twitter sentiment analysis and an LSTM neural network to predict election results. It involves collecting tweets related to political parties and candidates in India, cleaning the data, training an LSTM classifier on labeled tweets, and using the trained model to classify tweets as positive, negative or neutral sentiment and compare sentiment levels for each candidate. The goal is to analyze public sentiment expressed on Twitter and how it correlates with actual election outcomes.
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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.
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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.
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This document discusses sentiment analysis on Twitter data using machine learning techniques. It analyzes tweets to classify sentiment as positive, negative, or neutral. It uses Naive Bayes, SVM, and neural networks classifiers, evaluating each on accuracy. SVM performed best with 81.57% accuracy. The analyzed tweets were classified as 17.31% positive, 60.06% negative, and 22.62% neutral. Future work could improve accuracy and detect sarcasm, irony and humor in tweets.
Detailed Investigation of Text Classification and Clustering of Twitter Data ...ijtsrd
As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
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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.
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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.
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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.
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.
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The document describes a real-time Twitter sentiment analysis and visualization system called TwiSent. TwiSent uses a lexicon-based approach for sentiment analysis on tweets collected in real-time from Twitter using hashtags and keywords. It analyzes the sentiment of tweets as positive or negative and visualizes the results in a web application. The system aims to help organizations, political parties, and individuals better understand opinions on social media and make improved decisions.
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 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.
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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.
Sentiment Analysis of Twitter tweets using supervised classification technique IJERA Editor
Making use of social media for analyzing the perceptions of the masses over a product, event or a person has
gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as
the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets
on users' area of interest and analyze them. The extracted tweets are then segregated as positive, negative and
neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the
collected tweets to convert all letters to lowercase, eliminate special characters etc. which makes the
classification more efficient; the processed tweets are classified using a supervised classification technique. We
make use of Naive Bayes classifier to segregate the tweets as positive, negative and neutral. We use a set of
sample tweets to train the classifier. The percentage of the tweets in each category is then computed and the
result is represented graphically. The result can be used further to gain an insight into the views of the people
using Twitter about a particular topic that is being searched by the user. It can help corporate houses devise
strategies on the basis of the popularity of their product among the masses. It may help the consumers to make
informed choices based on the general sentiment expressed by the Twitter users on a product.
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.
IRJET- A Review on: Sentiment Polarity Analysis on Twitter Data from Diff...IRJET Journal
This document summarizes research on sentiment polarity analysis of Twitter data from different events. It discusses how Twitter data can be used for opinion mining and sentiment analysis. Several papers that used techniques like naive Bayes classifier, support vector machines, and dual sentiment analysis on Twitter data are summarized. The document also provides an overview of the key steps involved in a Twitter sentiment analysis system, including data collection, preprocessing, feature extraction, training a classification model, and evaluating accuracy. The goal of analyzing sentiments on Twitter is to understand public opinions on different topics and events.
This document discusses sentiment analysis of tweets from Twitter. It begins with an introduction to how social media allows people to share opinions and how analyzing sentiment can be useful. It then discusses previous work on sentiment analysis of Twitter data, focusing on techniques like Naive Bayes classification. The document outlines a proposed approach to collecting Twitter data using APIs, preprocessing the data by removing stop words and emoticons, and classifying sentiment using Naive Bayes. Finally, it discusses applications of sentiment analysis and potential areas for future work, such as handling multiple languages and semantic analysis.
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- Sentimental Analysis from Tweets to Find Positive, Negative,Neutra...IRJET Journal
This document discusses using sentiment analysis on tweets to determine positive, negative, and neutral opinions. It proposes using natural language processing and the naive bayes algorithm to more efficiently perform sentiment analysis. Data would be collected from tweets, cleaned, preprocessed using techniques like tokenization and stemming, then analyzed for sentiment. The results would be visualized using bar charts and pie charts to understand public opinions on topics. Topic modeling would also be used to identify frequently discussed topics. The goal is to help understand public and political views by analyzing social media posts.
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.
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.
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.
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.
IRJET- Design and Implementation of Sentiment Analyzer for Top Engineering Co...IRJET Journal
This document describes a study that developed a sentiment analyzer to analyze people's opinions on top engineering colleges in India using Twitter data. It involved collecting tweets related to three top engineering colleges in India from Twitter using APIs. The tweets were preprocessed by removing URLs, user mentions, digits, stop words, and applying other text cleaning steps. A maximum entropy classifier was then used to classify the sentiment expressed in each tweet as positive, negative or neutral. The goal was to analyze public sentiment towards the top engineering colleges based on what people were sharing on Twitter.
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
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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.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
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.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
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.
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.
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.