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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Akhi, Fatema Tuz Zohra Arefin, Mohammad Shamsul Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798331529765

The growing ubiquity of bilingual and multilingual communication presents serious difficulties for conventional Automatic Speech Recognition (ASR) system in the field of voice recognition. Code-switching is a major problem for traditional ASR systems because it enables two or more languages to be switched into one conversation. In this paper, the model can detect the language (whether Bangla or English) from a spoken sentence and also perform the ASR task from this mixed voice. For this context, XLSR Wav2vec 2.0 encoder has been used for feature extraction, CTC (Connectionist Temporal Classification) is used for ASR purpose and LID (Language Identification) for language identification task. By applying this approach, it is found that the Word Error Rate (WER) is 21.5% and the Language Identification (LID) accuracy is 70%. The suggested approach shows notable gains over any other models and provides a viable method for precise and effective ASR and LID in code switching. © 2024 IEEE.

关键词： Speech recognition

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CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT 4

CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification ...

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4th Workshop on Language Technology for Equality, Diversity, Inclusion, LT-EDI 2024

作者： Raihan, Abu Bakkar Siddique Rahman, Tanzim Rahman, Md. Tanvir Hossain, Jawad Ahsan, Shawly Das, Avishek Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798891760813

The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, "Stress Identification in Dravidian Languages," specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of 0.71 and 0.72, respectively, and securing the 15th position in Tamil code-mixed language and the 9th position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu. © 2024 Association for Computational Linguistics.

关键词： Learning systems

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Predicting Concentration of Particulate Matter (PM2.5) in Hamedan using Machine Learning Algorithms 15

Predicting Concentration of Particulate Matter (PM2.5) in Ha...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Ghassabpour, Anita Karim Abdoli, Hatam Mansoorizadeh, Muharram Seyedi, Saeid Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamedan Iran

ISBN: (纸本)9798331522254

Given that fine particles are one of the main origins of respiratory disorders, it is considered that PM2.5 is among the important contributors to air pollution and is a serious global health concern nowadays. This paper considers a new analytical approach for the prediction of PM2.5 concentration in Hamadan, Iran, with hopes of finding some ways to reduce the negative impacts of air pollution. During the last two years, the PM2.5 hourly data was gathered;they were preprocessed, and the outlier values were imputed using K-Nearest Neighbors techniques. To increase the accuracy, the estimation was improved by applying four machine learning models, namely, random forest, decision tree, support vector machine, and linear regression. Originality is represented by merging machine learning models with the time series model ARIMA. Thus, each model hybrid takes the strengths from all, giving a higher value of prediction of PM2.5 concentration. In this study many metrics such as MSE, RMSE, MAE, precision, and recall are applied for finding out the best model performance. Probably the most relevant outcome of our results is that the combination of linear regression and ARIMA returned a significant performance boost: MSE improved by 58%, while RMSE improved by 35%. This dramatic improvement underlines the predictive potential of hybrid models for air quality forecasting and forms a milestone in the study of PM2.5 prediction for the region. © 2024 IEEE.

关键词： Random forests

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CUET_Binary_Hackers@DravidianLangTech-EACL 2024: Sentiment Analysis using Transformer-Based Models in Code-Mixed and Transliterated Tamil and Tulu 4

CUET_Binary_Hackers@DravidianLangTech-EACL 2024: Sentiment A...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Eusha, Asrarul Hoque Farsi, Salman Islam, Ariful Hossain, Jawad Ahsan, Shawly Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798891760783

Textual Sentiment Analysis (TSA) delves into people’s opinions, intuitions, and emotions regarding any entity. Natural Language Processing (NLP) serves as a technique to extract subjective knowledge, determining whether an idea or comment leans positive, negative, neutral, or a mix thereof toward an entity. In recent years, it has garnered substantial attention from NLP researchers due to the vast availability of online comments and opinions. Despite extensive studies in this domain, sentiment analysis in low-resourced languages such as Tamil and Tulu needs help handling code-mixed and transliterated content. To address these challenges, this work focuses on sentiment analysis of code-mixed and transliterated Tamil and Tulu social media comments. It explored four machine learning (ML) approaches (LR, SVM, XGBoost, Ensemble), four deep learning (DL) methods (BiLSTM and CNN with fastText and Word2Vec), and four transformer-based models (m-BERT, MuRIL, L3Cube-IndicSBERT, and Distilm-BERT) for both languages. For Tamil, L3CubeIndicSBERT and ensemble approaches outperformed others, while m-BERT demonstrated superior performance among the models for Tulu. The presented models achieved the 3rd and 1st ranks by attaining macro F1-scores of 0.23 and 0.58 in Tamil and Tulu, respectively. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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Relationship Extraction from Electronic Health Records & Generating Knowledge Graph 4

Relationship Extraction from Electronic Health Records & Gen...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Tirpude, Shubhangi C. Shahu, Vedant R. Neema, Meghal G. Chatterjee, Rishika K. Wanve, Piyush H. Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350349856

The research delves into the process of extracting relationships from biomedical literature and building a knowledge graph using those relationships. Information retrieval, knowledge discovery, and data analysis are just a few of the uses for named entity extraction in biomedical research, which includes important entities such as Adverse Drug Effects (ADE), doses, drug names, and more. Using BERT's word contextual comprehension, the report investigates the use of the BioBERT model for named entity recognition and relationship extraction. When calibrated on Electronic Health Records (EHR) data, BioBERT - a biological text-specific model - helps with clinical decision-making and research by recognizing links between elements such as diseases, symptoms, and therapies. The use of Neo4j for knowledge graph generation, which permits a thorough representation of biomedical concepts and their relationships, is also highlighted in the paper. Information retrieval and analysis are made easier by this organized knowledge. This strategy has the potential to revolutionize biomedical research and healthcare by providing fresh perspectives and better results, despite ongoing challenges. More developments in this field are anticipated as a result of ongoing research and development. © 2024 IEEE.

关键词： Knowledge graph

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Contents-Based Spam Detection on Social Networks Using RoBERTa Embedding and Stacked BLSTM

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SN computer Science 2023年第4期4卷 380页

作者： Ghanem, Razan Erbay, Hasan Bakour, Khaled Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey Department of Computer Engineering University of Turkish Aeronautical Association Ankara Turkey

The use of social networks has become an integral part of our daily lives. Even though social networking sites offer many advantages, they also pose a number of problems for their users. One of the most famous problems is unwanted messages. It is not desirable for social network users to be bothered by annoying and time-wasting messages. These unwanted messages, which include ads, malicious content, and any low-quality content, are called spam. It is challenging to combat spam on social networks, because messages exchanged through social media are short, sparse, and may contain grammatical and spelling errors in addition to complex characters and special patterns. The main task to solve such a problem depends essentially on an appropriate representation of the text to increase the efficiency of the classifier. Therefore, in this study, we introduce a RoBERTa-based bi-directional Recurrent Neural Network model for spam detection on social networks. The RoBERTa model is used to learn contextualized word representations to improve the performance of the stacked BLSTM network. Moreover, a comparative study, in which we apply the most common transformer-based models, has been conducted as well to solve the spam problem. The experimental results on three benchmark data set state that our RoBERTa–BLSTM model outperforms all common models used to detect spam on social networks with an accuracy of 98.15%, 94.41%, and 99.74% on Twitter, YouTube, and SMS data sets, respectively. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BERT RoBERTa Spam detection Stacked BLSTM Transformer Word embedding

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Bug Classification Using Stacking Classifier 2

Bug Classification Using Stacking Classifier

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kusumaniswar, V. Chinta, Mukesh Shareff, Sk. Nawaz Vr Siddhartha Engineering College Computer Science & Engineering Department Andhra Pradesh India

ISBN: (纸本)9798350371055

Stacking Classifier is an ensemble learning technique in machine learning where the outcome of various base classifiers is forwarded as an input for meta classifier which makes the final decision. In any software industry a triage team will allocate the bugs raised in the software to the designated developers, nevertheless, there is a potential that they may give the bug to the incorrect category of developers, wasting time and effort. It will be simple for the triager to allocate the issue to the appropriate developer if he is able to accurately identify the kind of bug. Also studying the complete bug report and identifying the type of bug is time taking process for the developer. This system identifies the variety of bug using machine learning approach. The primary input to differentiate the type of bug is the description written for the bug report. The description is fed to the natural language processing (NLP) algorithm as an input for text processing, which performs operations like word tokenization, word lemmatization and converting text into numerical representation. BERT (Bidirectional Encoder Representations from Transformers) model is utilized to convert text into numerical data. This system uses Random Forest, Gradient Boosting and Support Vector Machine (SVM) as base classifiers and Logistic Regression as meta classifier. In this system, we can determine 3 kinds of bugs namely Concurrency, Semantic and Memory bugs using Stacking classifier. This paper presents a machine learning model which provides high precision than existing systems which utilizes other models like Decision Tree, Random Forest and Naive Bayes individually. © 2024 IEEE.

关键词： Bug report Bug Triage Data Processing Ensemble Learning Stacking Classifier

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A Multi-Material and Multi-Scenario Dataset for Additive and Subtractive Manufacturing Operations 29

A Multi-Material and Multi-Scenario Dataset for Additive and...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Uddin, Mohammad Gaiardelli, Sebastiano Lora, Michele Cheng, Dong Seon Fummi, Franco University of Verona Industrial Computer Engineering Laboratory Department of Engineering for Innovation Medicine Italy

ISBN: (纸本)9798350361230

Smart manufacturing systems often use data to optimize production processes. Artificial intelligence algorithms can be used to tune the parameters of production recipes to achieve the desired results. Indeed, such algorithms will require to be trained by using data collected while executing manufacturing operations. However, the required sets of data are rarely available to most production companies. This work-in-progress paper introduces the Print+Mill dataset: a collection of data related to additive and subtractive manufac-turing operations. The data are collected during the execution of various production recipes, utilizing different materials and process parameters. For each recipe, the dataset includes data on the materials and parameters used, sensor readings from the machinery, such as power consumption and temperature, and information on the quality of the resulting product. The data are collected in a complex research facility;in the future, we plan to extend the dataset by considering other manufacturing operations, materials, and types of field data. © 2024 IEEE.

关键词： Smart manufacturing

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Power System Reliability Evaluation Based on Sequential Monte Carlo Simulation Considering Multiple Failure Modes of Components

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 202-214页

作者： Wei Huang Bo Hu Changzheng Shao Wei Li Xiaozhe Wang Kaigui Xie C.Y.Chung State Key Laboratory of Power Transmission Equipment Technology School of Electrical EngineeringChongqing UniversityChongqingChina China Southern Power Grid GuangzhouChina Department of Electrical and Computer Engineering McGill UniversityMontrealCanada Department of Electrical and Electronic Engineering Hong Kong Polytechnic UniversityHong KongChina

The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

关键词： Power system reliability evaluation aging failure sequential Monte Carlo simulation

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