This paper aims to detect hate speech in multilingual text using deep learning techniques. Specifically, it focuses on English-Hindi code-mixed text from social media. The paper combines three existing datasets to create a larger consolidated dataset of over 20,000 tweets and comments annotated as hate speech or non-hate speech. It then applies and compares various machine learning and deep learning models on this dataset. The experimental results show that a CNN-BiLSTM deep learning model achieves the best performance with 87% accuracy, 82% precision, and 85% F1 score, outperforming existing approaches.
Hate speech detection on Indonesian text using word embedding method-global v...IAESIJAI
Hate speech is defined as communication directed toward a specific individual or group that involves hatred or anger and a language with solid arguments leading to someone's opinion can cause social conflict. It has a lot of potential for individuals to communicate their thoughts on an online platform because the number of Internet users globally, including in Indonesia, is continually rising. This study aims to observe the impact of pre-trained global vector (GloVe) word embedding on accuracy in the classification of hate speech and non-hate speech. The use of pre-trained GloVe (Indonesian text) and single and multi-layer long short-term memory (LSTM) classifiers has performance that is resistant to overfitting compared to pre-trainable embedding for hate-speech detection. The accuracy value is 81.5% on a single layer and 80.9% on a double-layer LSTM. The following job is to provide pre-trained with formal and non-formal language corpus; pre-processing to overcome non-formal words is very challenging.
An evolutionary approach to comparative analysis of detecting Bangla abusive ...journalBEEI
The use of Bangla abusive texts has been accelerated with the progressive use of social media. Through this platform, one can spread the hatred or negativity in a viral form. Plenty of research has been done on detecting abusive text in the English language. Bangla abusive text detection has not been done to a great extent. In this experimental study, we have applied three distinct approaches to a comprehensive dataset to obtain a better outcome. In the first study, a large dataset collected from Facebook and YouTube has been utilized to detect abusive texts. After extensive pre-processing and feature extraction, a set of consciously selected supervised machine learning classifiers i.e. multinomial Naïve Bayes (MNB), multi layer perceptron (MLP), support vector machine (SVM), decision tree, random forrest, stochastic gradient descent (SGD), ridge, perceptron and k-nearest neighbors (k-NN) has been applied to determine the best result. The second experiment is conducted by constructing a balanced dataset by random under sampling the majority class and finally, a Bengali stemmer is employed on the dataset and then the final experiment is conducted. In all three experiments, SVM with the full dataset obtained the highest accuracy of 88%.
SENTIMENT ANALYSIS OF MIXED CODE FOR THE TRANSLITERATED HINDI AND MARATHI TEXTSijnlc
The evolution of information Technology has led to the collection of large amount of data, the volume of
which has increased to the extent that in last two years the data produced is greater than all the data ever
recorded in human history. This has necessitated use of machines to understand, interpret and apply data,
without manual involvement. A lot of these texts are available in transliterated code-mixed form, which due
to the complexity are very difficult to analyze. The work already performed in this area is progressing at
great pace and this work hopes to be a way to push that work further. The designed system is an effort
which classifies Hindi as well as Marathi text transliterated (Romanized) documents automatically using
supervised learning methods (KNN), Naïve Bayes and Support Vector Machine (SVM)) and ontology based
classification; and results are compared to in order to decide which methodology is better suited in
handling of these documents. As we will see, the plain machine learning algorithm applications are just as
or in many cases are much better in performance than the more analytical approach.
Hate Speech Recognition System through NLP and Deep LearningIRJET Journal
The document describes a proposed system for recognizing hate speech through natural language processing and deep learning techniques. It discusses how hate speech on social media platforms is a growing problem. The proposed system uses techniques like TF-IDF, entropy estimation, and a fuzzy artificial neural network for hate speech recognition. The system preprocesses text data by removing special symbols, applying stemming, and removing stop words. It then classifies text as hate speech or not hate speech using the natural language processing and deep learning models. The authors conducted experiments that showed the system achieved highly positive results in hate speech recognition performance.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimizatio...IJECEIAES
This document summarizes a research paper that proposes using a tree-based pipeline optimization tool (TPOT) to improve sentiment classification of dialectal Arabic texts. The paper provides background on sentiment analysis and challenges in analyzing informal Arabic texts. It then discusses related work applying TPOT and AutoML techniques to optimize machine learning for various tasks. The proposed approach uses TPOT for sentiment analysis of three Arabic dialect datasets to automatically optimize hyperparameters and improve over similar prior work.
Machine Learning Approach to Classify Twitter Hate Speechijtsrd
In this modern age, social media platforms have become indispensable tools for communication and information sharing. However, this unprecedented connectivity has also given rise to a concerning proliferation of hate speech and offensive content. This research article presents a comprehensive study on the development and evaluation of machine learning ML models for the automatic detection of hate speech on Twitter. We leverage a diverse dataset collected from Twitter, encompassing a wide range of hate speech categories, including hate speech targeting race, gender, religion, and more. To address the multifaceted nature of hate speech, we employ a hybrid approach that combines traditional natural language processing NLP techniques with state of the art machine learning algorithms. Our methodology involves extensive preprocessing of the text data, including tokenization, stemming, and feature extraction. We then experiment with various machine learning algorithms, including Naïve Bayes NB , K nearest Neighbor KNN , Random Forest RF , and Support Vector Machines SVM . The models are trained and fine tuned on a labeled dataset and evaluated using robust metrics to assess their performance. Subrata Saha | Md. Motinur Rahman | Md. Mahbub Alam "Machine Learning Approach to Classify Twitter Hate Speech" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59873.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59873/machine-learning-approach-to-classify-twitter-hate-speech/subrata-saha
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
This document presents a framework for Arabic concept-level sentiment analysis using SenticNet. It discusses existing sentiment analysis approaches and focuses on concept-level sentiment analysis, which classifies text based on semantics rather than syntax. The authors modify SenticNet to suit Arabic and test it on a multi-domain Arabic dataset. Syntactic patterns are used to extract concepts from sentences, which are then translated to English and matched to SenticNet concepts to determine polarity. An accuracy of 70% was obtained when testing the generated lexicon on the dataset. The lexicon containing 69k unique concepts covers reviews from multiple domains and is made publicly available.
AUTOMATIC DETECTION AND LANGUAGE IDENTIFICATION OF MULTILINGUAL DOCUMENTSIRJET Journal
The document describes a study that aims to handle the task of detecting offensive language in multilingual documents using machine learning models. The proposed framework consists of three phases: preprocessing text, representing text using BERT models, and classifying text into offensive and non-offensive classes. The study examines different strategies for handling multilingualism, such as creating one classification model for multiple languages or using translation to convert all texts to one language before classification. Experimental results on a bilingual dataset show that the translation-based approach using Arabic BERT achieves over 93% F1-score and 91% precision for offensive language detection in multilingual texts.
Hate speech detection on Indonesian text using word embedding method-global v...IAESIJAI
Hate speech is defined as communication directed toward a specific individual or group that involves hatred or anger and a language with solid arguments leading to someone's opinion can cause social conflict. It has a lot of potential for individuals to communicate their thoughts on an online platform because the number of Internet users globally, including in Indonesia, is continually rising. This study aims to observe the impact of pre-trained global vector (GloVe) word embedding on accuracy in the classification of hate speech and non-hate speech. The use of pre-trained GloVe (Indonesian text) and single and multi-layer long short-term memory (LSTM) classifiers has performance that is resistant to overfitting compared to pre-trainable embedding for hate-speech detection. The accuracy value is 81.5% on a single layer and 80.9% on a double-layer LSTM. The following job is to provide pre-trained with formal and non-formal language corpus; pre-processing to overcome non-formal words is very challenging.
An evolutionary approach to comparative analysis of detecting Bangla abusive ...journalBEEI
The use of Bangla abusive texts has been accelerated with the progressive use of social media. Through this platform, one can spread the hatred or negativity in a viral form. Plenty of research has been done on detecting abusive text in the English language. Bangla abusive text detection has not been done to a great extent. In this experimental study, we have applied three distinct approaches to a comprehensive dataset to obtain a better outcome. In the first study, a large dataset collected from Facebook and YouTube has been utilized to detect abusive texts. After extensive pre-processing and feature extraction, a set of consciously selected supervised machine learning classifiers i.e. multinomial Naïve Bayes (MNB), multi layer perceptron (MLP), support vector machine (SVM), decision tree, random forrest, stochastic gradient descent (SGD), ridge, perceptron and k-nearest neighbors (k-NN) has been applied to determine the best result. The second experiment is conducted by constructing a balanced dataset by random under sampling the majority class and finally, a Bengali stemmer is employed on the dataset and then the final experiment is conducted. In all three experiments, SVM with the full dataset obtained the highest accuracy of 88%.
SENTIMENT ANALYSIS OF MIXED CODE FOR THE TRANSLITERATED HINDI AND MARATHI TEXTSijnlc
The evolution of information Technology has led to the collection of large amount of data, the volume of
which has increased to the extent that in last two years the data produced is greater than all the data ever
recorded in human history. This has necessitated use of machines to understand, interpret and apply data,
without manual involvement. A lot of these texts are available in transliterated code-mixed form, which due
to the complexity are very difficult to analyze. The work already performed in this area is progressing at
great pace and this work hopes to be a way to push that work further. The designed system is an effort
which classifies Hindi as well as Marathi text transliterated (Romanized) documents automatically using
supervised learning methods (KNN), Naïve Bayes and Support Vector Machine (SVM)) and ontology based
classification; and results are compared to in order to decide which methodology is better suited in
handling of these documents. As we will see, the plain machine learning algorithm applications are just as
or in many cases are much better in performance than the more analytical approach.
Hate Speech Recognition System through NLP and Deep LearningIRJET Journal
The document describes a proposed system for recognizing hate speech through natural language processing and deep learning techniques. It discusses how hate speech on social media platforms is a growing problem. The proposed system uses techniques like TF-IDF, entropy estimation, and a fuzzy artificial neural network for hate speech recognition. The system preprocesses text data by removing special symbols, applying stemming, and removing stop words. It then classifies text as hate speech or not hate speech using the natural language processing and deep learning models. The authors conducted experiments that showed the system achieved highly positive results in hate speech recognition performance.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimizatio...IJECEIAES
This document summarizes a research paper that proposes using a tree-based pipeline optimization tool (TPOT) to improve sentiment classification of dialectal Arabic texts. The paper provides background on sentiment analysis and challenges in analyzing informal Arabic texts. It then discusses related work applying TPOT and AutoML techniques to optimize machine learning for various tasks. The proposed approach uses TPOT for sentiment analysis of three Arabic dialect datasets to automatically optimize hyperparameters and improve over similar prior work.
Machine Learning Approach to Classify Twitter Hate Speechijtsrd
In this modern age, social media platforms have become indispensable tools for communication and information sharing. However, this unprecedented connectivity has also given rise to a concerning proliferation of hate speech and offensive content. This research article presents a comprehensive study on the development and evaluation of machine learning ML models for the automatic detection of hate speech on Twitter. We leverage a diverse dataset collected from Twitter, encompassing a wide range of hate speech categories, including hate speech targeting race, gender, religion, and more. To address the multifaceted nature of hate speech, we employ a hybrid approach that combines traditional natural language processing NLP techniques with state of the art machine learning algorithms. Our methodology involves extensive preprocessing of the text data, including tokenization, stemming, and feature extraction. We then experiment with various machine learning algorithms, including Naïve Bayes NB , K nearest Neighbor KNN , Random Forest RF , and Support Vector Machines SVM . The models are trained and fine tuned on a labeled dataset and evaluated using robust metrics to assess their performance. Subrata Saha | Md. Motinur Rahman | Md. Mahbub Alam "Machine Learning Approach to Classify Twitter Hate Speech" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59873.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59873/machine-learning-approach-to-classify-twitter-hate-speech/subrata-saha
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
This document presents a framework for Arabic concept-level sentiment analysis using SenticNet. It discusses existing sentiment analysis approaches and focuses on concept-level sentiment analysis, which classifies text based on semantics rather than syntax. The authors modify SenticNet to suit Arabic and test it on a multi-domain Arabic dataset. Syntactic patterns are used to extract concepts from sentences, which are then translated to English and matched to SenticNet concepts to determine polarity. An accuracy of 70% was obtained when testing the generated lexicon on the dataset. The lexicon containing 69k unique concepts covers reviews from multiple domains and is made publicly available.
AUTOMATIC DETECTION AND LANGUAGE IDENTIFICATION OF MULTILINGUAL DOCUMENTSIRJET Journal
The document describes a study that aims to handle the task of detecting offensive language in multilingual documents using machine learning models. The proposed framework consists of three phases: preprocessing text, representing text using BERT models, and classifying text into offensive and non-offensive classes. The study examines different strategies for handling multilingualism, such as creating one classification model for multiple languages or using translation to convert all texts to one language before classification. Experimental results on a bilingual dataset show that the translation-based approach using Arabic BERT achieves over 93% F1-score and 91% precision for offensive language detection in multilingual texts.
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
An Analytical Survey on Hate Speech Recognition through NLP and Deep LearningIRJET Journal
The document discusses hate speech recognition through natural language processing and deep learning. It provides an overview of past work on hate speech detection using various techniques like pattern-based methods, multi-class text classification, hybrid neural networks, convolutional neural networks and feature-based machine learning models. The literature review analyzes these previous studies and their methods and limitations to help develop an improved methodology for hate speech recognition.
Hate Speech Identification Using Machine LearningIRJET Journal
This document discusses a study that used machine learning to identify hate speech on social media. The researchers created a model using subjectivity analysis, semantic features, and a hate speech lexicon to classify tweets. They extracted subjective words and identified hate-related verbs to build the lexicon. They also looked at frequently used noun phrases related to hateful tweets. The model classified tweets as strongly hateful, weakly hateful, or non-hateful based on criteria involving the hate lexicon. The researchers aimed to automate hate speech detection, which is currently a slow, manual process, and address a lack of prior work on identifying hate speech in Hindi.
SARCASM AS A CONTRADICTION BETWEEN A TWEET AND ITS TEMPORAL FACTS: A PATTERNB...kevig
In the context of Indian languages, sarcasm detection in Hindi is a tedious job as it is rich in morphology and complex in structure. The annotated resources for sarcastic Hindi sentences are almost negligible for machine learning analysis. Here, we propose a pattern-based framework for sarcasm detection in Hindi tweets. It has been observed that a tweet is sarcastic if it contradicts its temporal facts intentionally. The temporal fact is a collection of time-dependent facts which may change over the period. We used Hindi news with timestamp as a corpus of temporal facts. The timestamp describes the fact period of any entity. In this research, a temporal fact is represented as a pair. To form a pair, one need to extract triplets i.e. subject, verb and object for every sentence. Next, a key is formed using subject and verb together. The value is formed using object and timestamp together. To predict the sarcastic tweet; one needs to extract the triplets from input tweet and form a pair. Now, the pair of the input tweet is mapped with related pair in the corpus of temporal facts and are checked if they coincide. If they contradict, the input tweet is considered as sarcastic. The achieved accuracy of the proposed approach outperforms the state-of-the-arts techniques for Hindi sarcasm detection as it attains an accuracy of 82.8%.
SARCASM AS A CONTRADICTION BETWEEN A TWEET AND ITS TEMPORAL FACTS: A PATTERNB...ijnlc
This document proposes a pattern-based framework for detecting sarcasm in Hindi tweets. It observes that a tweet is sarcastic if it contradicts its temporal facts intentionally. Temporal facts are collected from Hindi news articles with timestamps, represented as subject-verb-object triplets. To detect sarcasm, a tweet is mapped to related temporal facts; if they contradict, the tweet is sarcastic. Evaluating on 500 tweets, the approach achieves 82.8% accuracy, outperforming prior techniques for Hindi sarcasm detection.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document discusses a study on detecting cyberbullying using machine learning and deep learning techniques. Specifically, it examines using a hybrid model combining K-Nearest Neighbors, Support Vector Machine, and Random Forest algorithms, as well as a Convolutional Neural Network. The study uses a Twitter dataset to classify tweets as not bullying, racism, or sexism. It finds that the CNN model produces more accurate predictions than the hybrid stacking algorithm. The document provides background on related work applying machine and deep learning to cyberbullying detection, particularly using content-based and user-based approaches.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document summarizes a research paper that studied the detection of cyberbullying using machine learning and deep learning techniques. Specifically, it used a hybrid model combining KNN, SVM and Random Forest algorithms (stacking algorithm) and a Convolutional Neural Network (CNN) on a Twitter dataset. The stacking algorithm achieved an accuracy of X% while the CNN achieved a higher accuracy of Y%. A comparison of the two models found that CNN produced a more precise prediction of cyberbullying. The document also reviewed related work on cyberbullying detection using content-based, user-based and network-based approaches with machine learning algorithms like SVM, Naive Bayes and deep learning methods like CNN.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
International Journal of Computer Science, Engineering and Applications (IJCSEA)IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
Analyzing sentiment dynamics from sparse text coronavirus disease-19 vaccina...IJECEIAES
Social media platforms enable people exchange their thoughts, reactions, emotions regarding all aspects of their lives. Therefore, sentiment analysis using textual data is widely practiced field. Due to large textual content available on social media, sentiment analysis is usually considered a text classification task. The high feature dimension is an important issue that needs to be resolved by examining text meaningfully. The proposed study considers a case study of coronavirus (COVID) vaccination to conclude public opinions about prospects for vaccination. Text corpus of tweets is collected, published between December 12, 2020, and July 13, 2021 is considered. The proposed model is developed considering phase-by-phase data analysis process, followed by an assessment of important information about the collected tweets on coronavirus disease (COVID-19) vaccine using two sentiment analyzer methods and probabilistic models for validation and knowledge analysis. The result indicated that public sentiment is more positive than negative. The study also presented statistics of trends in vaccination progress in the top countries from early 2021 to July 2021. The scope of study is enormous regarding sentiment analysis based on keyword and document modeling. The proposed work offers an effective mechanism for a decision-making system to understand public opinion and accordingly assists policymakers in health measures and vaccination campaigns.
Cyber bullying Detection based on Semantic-Enhanced Marginalized Denoising Au...dbpublications
As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising autoencoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text. Comprehensive experiments on two public cyberbullying corpora (Twitter and MySpace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods..
A hybrid approach based on personality traits for hate speech detection in Ar...IJECEIAES
This paper proposes a hybrid approach for detecting hate speech in Arabic social media. The approach has two phases: the first phase infers personality trait features from text using a dataset of Arabic tweets annotated with personality labels. Machine learning classifiers are trained to predict the big five personality traits. The second phase identifies hate speech using additional features derived from the personality traits, along with text representation techniques like TF-IDF and word embeddings. Experimental results on an Arabic hate speech dataset show the proposed approach achieves an F1 score of 82.3%, outperforming previous work. The study presents a novel method for hate speech detection based on incorporating insights from personality literature.
Fake News Detection Using Machine LearningIRJET Journal
This document proposes a machine learning approach for detecting fake news using support vector machines. It discusses preprocessing news data using techniques like TF-IDF, extracting features related to text, date, source and author, and training a support vector machine classifier on the preprocessed data. The proposed system architecture involves preprocessing, training a model on the training data, validating it on test data, adjusting parameters to improve accuracy, and then using the model to classify new unlabeled news. Prior research that used techniques like n-gram analysis, naive Bayes classifiers and linear support vector machines for fake news detection are also reviewed. The conclusion is that the proposed approach using support vector machines can help identify fake news effectively.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
Application Of Sentiment Lexicons On Movies Transcripts To Detect Violence In...Sara Alvarez
The document summarizes a research paper that used sentiment analysis techniques to detect violence in video transcripts. It applied two sentiment lexicons (English SentiWordNet and Vader Package) to 100 annotated video transcripts to classify videos as violent or non-violent. The Vader Package achieved 75% accuracy, outperforming English SentiWordNet which achieved 66% accuracy when applying part-of-speech tagging to all words, and 58% accuracy when only applying it to adjectives. The document also reviews related work on sentiment analysis, violence detection, and using video transcripts for tasks like genre classification and emotion detection.
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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An Analytical Survey on Hate Speech Recognition through NLP and Deep LearningIRJET Journal
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This document discusses a study that used machine learning to identify hate speech on social media. The researchers created a model using subjectivity analysis, semantic features, and a hate speech lexicon to classify tweets. They extracted subjective words and identified hate-related verbs to build the lexicon. They also looked at frequently used noun phrases related to hateful tweets. The model classified tweets as strongly hateful, weakly hateful, or non-hateful based on criteria involving the hate lexicon. The researchers aimed to automate hate speech detection, which is currently a slow, manual process, and address a lack of prior work on identifying hate speech in Hindi.
SARCASM AS A CONTRADICTION BETWEEN A TWEET AND ITS TEMPORAL FACTS: A PATTERNB...kevig
In the context of Indian languages, sarcasm detection in Hindi is a tedious job as it is rich in morphology and complex in structure. The annotated resources for sarcastic Hindi sentences are almost negligible for machine learning analysis. Here, we propose a pattern-based framework for sarcasm detection in Hindi tweets. It has been observed that a tweet is sarcastic if it contradicts its temporal facts intentionally. The temporal fact is a collection of time-dependent facts which may change over the period. We used Hindi news with timestamp as a corpus of temporal facts. The timestamp describes the fact period of any entity. In this research, a temporal fact is represented as a pair. To form a pair, one need to extract triplets i.e. subject, verb and object for every sentence. Next, a key is formed using subject and verb together. The value is formed using object and timestamp together. To predict the sarcastic tweet; one needs to extract the triplets from input tweet and form a pair. Now, the pair of the input tweet is mapped with related pair in the corpus of temporal facts and are checked if they coincide. If they contradict, the input tweet is considered as sarcastic. The achieved accuracy of the proposed approach outperforms the state-of-the-arts techniques for Hindi sarcasm detection as it attains an accuracy of 82.8%.
SARCASM AS A CONTRADICTION BETWEEN A TWEET AND ITS TEMPORAL FACTS: A PATTERNB...ijnlc
This document proposes a pattern-based framework for detecting sarcasm in Hindi tweets. It observes that a tweet is sarcastic if it contradicts its temporal facts intentionally. Temporal facts are collected from Hindi news articles with timestamps, represented as subject-verb-object triplets. To detect sarcasm, a tweet is mapped to related temporal facts; if they contradict, the tweet is sarcastic. Evaluating on 500 tweets, the approach achieves 82.8% accuracy, outperforming prior techniques for Hindi sarcasm detection.
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document discusses a study on detecting cyberbullying using machine learning and deep learning techniques. Specifically, it examines using a hybrid model combining K-Nearest Neighbors, Support Vector Machine, and Random Forest algorithms, as well as a Convolutional Neural Network. The study uses a Twitter dataset to classify tweets as not bullying, racism, or sexism. It finds that the CNN model produces more accurate predictions than the hybrid stacking algorithm. The document provides background on related work applying machine and deep learning to cyberbullying detection, particularly using content-based and user-based approaches.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document summarizes a research paper that studied the detection of cyberbullying using machine learning and deep learning techniques. Specifically, it used a hybrid model combining KNN, SVM and Random Forest algorithms (stacking algorithm) and a Convolutional Neural Network (CNN) on a Twitter dataset. The stacking algorithm achieved an accuracy of X% while the CNN achieved a higher accuracy of Y%. A comparison of the two models found that CNN produced a more precise prediction of cyberbullying. The document also reviewed related work on cyberbullying detection using content-based, user-based and network-based approaches with machine learning algorithms like SVM, Naive Bayes and deep learning methods like CNN.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular social media platform. Social media analytics helps make informed decisions based on people's needs and opinions. This information, when properly perceived provides valuable insights into different domains, such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet) algorithms. The experiments use different data processing steps including trigrams, without trigrams, hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags helps improve the topic inference results with a better coherence score.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
International Journal of Computer Science, Engineering and Applications (IJCSEA)IJCSEA Journal
International Journal of Computer Science, Engineering and Applications (IJCSEA) is an open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer science, Engineering and Applications. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science, Engineering and Applications.
ANALYSIS OF TOPIC MODELING WITH UNPOOLED AND POOLED TWEETS AND EXPLORATION OF...IJCSEA Journal
In this digital era, social media is an important tool for information dissemination. Twitter is a popular
social media platform. Social media analytics helps make informed decisions based on people's needs and
opinions. This information, when properly perceived provides valuable insights into different domains,
such as public policymaking, marketing, sales, and healthcare. Topic modeling is an unsupervised
algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet
Allocation (LDA) topic model algorithm. We collected tweets with hashtags related to corona virus related
discussions. This study compares regular LDA and LDA based on collapsed Gibbs sampling (LDAMallet)
algorithms. The experiments use different data processing steps including trigrams, without trigrams,
hashtags, and without hashtags. This study provides a comprehensive analysis of LDA for short text
messages using un-pooled and pooled tweets. The results suggest that a pooling scheme using hashtags
helps improve the topic inference results with a better coherence score.
Analyzing sentiment dynamics from sparse text coronavirus disease-19 vaccina...IJECEIAES
Social media platforms enable people exchange their thoughts, reactions, emotions regarding all aspects of their lives. Therefore, sentiment analysis using textual data is widely practiced field. Due to large textual content available on social media, sentiment analysis is usually considered a text classification task. The high feature dimension is an important issue that needs to be resolved by examining text meaningfully. The proposed study considers a case study of coronavirus (COVID) vaccination to conclude public opinions about prospects for vaccination. Text corpus of tweets is collected, published between December 12, 2020, and July 13, 2021 is considered. The proposed model is developed considering phase-by-phase data analysis process, followed by an assessment of important information about the collected tweets on coronavirus disease (COVID-19) vaccine using two sentiment analyzer methods and probabilistic models for validation and knowledge analysis. The result indicated that public sentiment is more positive than negative. The study also presented statistics of trends in vaccination progress in the top countries from early 2021 to July 2021. The scope of study is enormous regarding sentiment analysis based on keyword and document modeling. The proposed work offers an effective mechanism for a decision-making system to understand public opinion and accordingly assists policymakers in health measures and vaccination campaigns.
Cyber bullying Detection based on Semantic-Enhanced Marginalized Denoising Au...dbpublications
As a side effect of increasingly popular social media, cyberbullying has emerged as a serious problem afflicting children, adolescents and young adults. Machine learning techniques make automatic detection of bullying messages in social media possible, and this could help to construct a healthy and safe social media environment. In this meaningful research area, one critical issue is robust and discriminative numerical representation learning of text messages. In this paper, we propose a new representation learning method to tackle this problem. Our method named Semantic-Enhanced Marginalized Denoising Auto-Encoder (smSDA) is developed via semantic extension of the popular deep learning model stacked denoising autoencoder. The semantic extension consists of semantic dropout noise and sparsity constraints, where the semantic dropout noise is designed based on domain knowledge and the word embedding technique. Our proposed method is able to exploit the hidden feature structure of bullying information and learn a robust and discriminative representation of text. Comprehensive experiments on two public cyberbullying corpora (Twitter and MySpace) are conducted, and the results show that our proposed approaches outperform other baseline text representation learning methods..
A hybrid approach based on personality traits for hate speech detection in Ar...IJECEIAES
This paper proposes a hybrid approach for detecting hate speech in Arabic social media. The approach has two phases: the first phase infers personality trait features from text using a dataset of Arabic tweets annotated with personality labels. Machine learning classifiers are trained to predict the big five personality traits. The second phase identifies hate speech using additional features derived from the personality traits, along with text representation techniques like TF-IDF and word embeddings. Experimental results on an Arabic hate speech dataset show the proposed approach achieves an F1 score of 82.3%, outperforming previous work. The study presents a novel method for hate speech detection based on incorporating insights from personality literature.
Fake News Detection Using Machine LearningIRJET Journal
This document proposes a machine learning approach for detecting fake news using support vector machines. It discusses preprocessing news data using techniques like TF-IDF, extracting features related to text, date, source and author, and training a support vector machine classifier on the preprocessed data. The proposed system architecture involves preprocessing, training a model on the training data, validating it on test data, adjusting parameters to improve accuracy, and then using the model to classify new unlabeled news. Prior research that used techniques like n-gram analysis, naive Bayes classifiers and linear support vector machines for fake news detection are also reviewed. The conclusion is that the proposed approach using support vector machines can help identify fake news effectively.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
Application Of Sentiment Lexicons On Movies Transcripts To Detect Violence In...Sara Alvarez
The document summarizes a research paper that used sentiment analysis techniques to detect violence in video transcripts. It applied two sentiment lexicons (English SentiWordNet and Vader Package) to 100 annotated video transcripts to classify videos as violent or non-violent. The Vader Package achieved 75% accuracy, outperforming English SentiWordNet which achieved 66% accuracy when applying part-of-speech tagging to all words, and 58% accuracy when only applying it to adjectives. The document also reviews related work on sentiment analysis, violence detection, and using video transcripts for tasks like genre classification and emotion detection.
Similar to Hate Speech Detection in multilingual Text using Deep 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.
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.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.