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Hate Speech Detection in Marathi Tweets using Stacked Deep Learning models 15

Hate Speech Detection in Marathi Tweets using Stacked Deep L...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shedge, Prachi Kamalkar, Siddhi Gupta, Deepa Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Digital platforms have revolutionized communication, but they have also led to an increase in hate speech, making it challenging to create welcoming online groups. The purpose of this study is to close this gap by using deep learning models to detect hate speech in Marathi, a regional language of India. The researchers use advanced word embeddings and DL models like Long Short-Term Memory and Bidirectional LSTM to demonstrate the efficacy of these methods. The study aims to build strong hate speech detection systems that can be extended to other low-resource languages, creating safer online spaces. The study emphasizes the importance of contextually and culturally appropriate data in enhancing model accuracy. In order to strengthen their models' training and validation and guarantee reliable performance in real-world applications, the researchers also intend to integrate real-world social media data. The study closes a significant gap in regional language processing by concentrating on Marathi and opens the door for more inclusive and thorough hate speech detection frameworks across linguistic landscapes. © 2024 IEEE.

关键词： Embeddings

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SafeComm: An IOT-based System for enhancing rural child safety using LoRa Technology 2

SafeComm: An IOT-based System for enhancing rural child safe...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Thangam, S. Gamidi, Rohan Surya, Thotapalli Sri Saragadam, Sneha Kumari, J. Jesy Janet Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350388916

The safety of kids who play outside while travelling in rural areas is a big concern. Safecomm's new technical fix combines LoRa, NFC, as well as UVC cam systems. Thereby, the gadget has been engineered by merging LoRa modules, NFC tags, GSM module and ESP cams for real-time tracking of where their children are and instant alerting their parents or schools. Besides, when there is an emergency, the wearer can choose to press the emergency button for the SOS family contacts and the emergency services organisations to be alerted in case the battery level is still good. If required, images can also be captured and saved on the cloud storage. This IoT product leverages LoRa, NFC, and an UVC cam to cater for safety issues relating to modes of transport for rural schools. Every day when children mobile around, this machine secures them. The attendance is made easy and quick response in cases of emergency is also possible through this technology so the risks are averted. This device ensures that children are safe when they move about every day. These technologies make keeping track of attendance easier and being able to quickly respond during emergencies so that dangers are mitigated, therefore parents as well as teachers can be less worried. This undertaking is seen as one important stride toward using internet of things (IoT) technology for ensuring children from outside urban areas are safe and boosting safety inside schools or while commuting using linked transportation means. © 2024 IEEE.

关键词： Emergency services

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Echoes of Truth: Unraveling Homophily in Attributed Networks for Rumor Detection 5

Echoes of Truth: Unraveling Homophily in Attributed Networks...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Rithish, S.V. Prabu, C.R. Anuush, M.B. Deepthi, L.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The surge in information and misinformation during the COVID-19 pandemic, particularly regarding 5G-related rumors, was prominent on various social media platforms, notably Twitter. This study addresses the pervasive issue of rumor propagation on Twitter by employing diverse machine learning models, clustering techniques, and node detection algorithms to gain insights into the dynamics of rumor dissemination. The primary focus is identifying influential individuals who significantly impact the spread of these rumors and had an accuracy of 0.86 while classifying various labels. The study reveals network structures and key influencers within each cluster by categorizing users into clusters. While it is commonly observed that users with a more extensive follower base have a broader impact on rumor dissemination, our innovative approach integrates advanced community detection algorithms and methods, the Overlapnodedetect algorithm and Rumorsourcetrace algorithm for identifying influential characters to uncover latent homophily structures within these clusters. The objective is identifying crucial users responsible for initiating and perpetuating these rumors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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Harnessing Diversity in Face Recognition: A Voting and Bagging Ensemble Approach

Harnessing Diversity in Face Recognition: A Voting and Baggi...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Aniket Sriram, Suthir Nivethitha, V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350382723

Facial recognition plays a crucial role in various applications, as it tackles challenges such as changes in lighting, position, and facial expressions. This study aims to explore the effectiveness of ensemble learning methods, specifically the voting classifier and bagging classifier, in enhancing the accuracy of face recognition. We extensively examine our ensemble technique, leveraging the diversity that emerges from combining multiple classifiers. The voting classifier showcases superior accuracy (0.91), underscoring the effectiveness of blending numerous models. The bagging classifier, even though it has achieved a respectable accuracy of 0.72, demonstrates a noteworthy improvement compared to individual classifiers. These findings emphasize the effectiveness of ensemble learning in alleviating the challenges encountered in face recognition tasks. Improving facial recognition systems in real-life situations is one useful piece of advice that this research offers, as it advances our knowledge of ensemble learning. © 2024 IEEE.

关键词： Learning systems

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Machine Learning Strategies for Telecom Churn Prediction 15

Machine Learning Strategies for Telecom Churn Prediction

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Poojitha, D. Darak, Tanmay Samaddar, Ushnish Venugopal, Manju Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The ability to predict customer churn represents a critical component in the telecom industry's quest for enhanced customer retention and long-term viability. In this research, several standalone machine learning algorithms, including gradient boosting and decision trees, have been applied to a dataset comprised of customer demographics and service usage patterns. The application of rigorous hyperparameter tuning and cross-validation processes has significantly improved the performance metrics, such as accuracy, precision, recall, and F1 score, of these models. Furthermore, the study incorporates two advanced ensemble techniques to refine the prediction accuracy. The combination of Random Forest and Gradient Boost achieved an accuracy of 85.7%, while the integration of AdaBoost and CatBoost resulted in an accuracy of 87.3%. This approach provides telecom operators with a potent tool to proactively identify and engage customers at risk of churn, thus enabling timely and targeted retention strategies that enhance customer satisfaction and contribute to the sustainability of telecom enterprises. ©2024 IEEE.

关键词： Sales

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Octopus: A Latent Diffusion Model for Enhanced Text-Driven Manipulation in Image Synthesis

Octopus: A Latent Diffusion Model for Enhanced Text-Driven M...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Nithin Skantha, M. Meghadharsan, B. Sri Vignesh, C. Thiruselvan, J. Anuragi, Arti Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350376876

By combining user interactivity and precise control over image attributes, the applications of text-based image synthesis can be vastly expanded. Thanks to recent advancements in developing state-of-the-art text-to-image models, we can now generate images with high fidelity and diversity. While a plethora of research focuses on improving the fidelity and diversity of images generated using text prompts, less focus is given to controlling the attributes or characteristics of the generated images. On the other hand, there have been advancements in image editing through text prompts, but most of these models lack the understanding of spatial knowledge and precise control over the objects present in the image, which makes the edited images lose much of their characteristics. To address these challenges, this study proposes a three-step methodology to introduce our text-driven image editing conditional diffusion model called 'Octopus,' which has precise control over image attributes. To bring in control over image attributes, this study uses a similar methodology proposed in a recent study, which uses rich text to control image attributes while exploring different formats, including bold, footnotes, font size, and font color. Then this model is used along with a fine-tuned Large Language Model (Gemma), to create a dataset of image editing examples to train Octopus, which, given an input image and edit instructions, generates the precise edited image during inference. This study demonstrates the potential of this approach with a variety of compelling results. © 2024 IEEE.

关键词： Shellfish

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Enhanced Multi-Class Fetal Plane Detection with Limb Localization in Ultrasound Images 2

Enhanced Multi-Class Fetal Plane Detection with Limb Localiz...

引用

2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Sindhu, Kolla Gnapika Annamalai, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

In this novel study, we address the critical need for automated interpretation and analysis of ultrasound images in the context of fetal development assessment. The current large-scale retrospective analyses suffer from the laborious and specialized process of manually identifying standard fetal ultrasound planes, requiring sonographer expertise. Our innovative approach leverages state-of-the-art deep-learning techniques, specifically vision transformers, to automate the detection of standard fetal ultrasound planes. Our methodology involves the integration of two datasets, with a particular emphasis on musculoskeletal planes, notably fetal limbs. The resulting comprehensive classification model comprises eight distinct classes: fetal limbs, fetal abdomen, fetal thorax, fetal femur, maternal cervix, and fetal arms. Notably, the application of vision transformers, specifically ViT -L16 (Vision Transformer), to mid-pregnancy prenatal screening achieved a remarkable test accuracy of 97.2%. The primary goal of incorporating vision transformers into prenatal screening is to enhance the efficiency and accuracy of identifying critical anatomical structures. This advancement facilitates the prompt evaluation of fetal growth metrics and enables early identification of irregularities. Serving as a valuable tool for ultrasound technicians, our study signifies a significant leap in transforming the assessment of fetal well-being through ultrasound techniques. Moreover, it sets new benchmarks for detecting abnormal fetal development within ultrasound visuals, contributing to the ongoing evolution of fetal health assessment methodologies. © 2024 IEEE.

关键词： Median filters

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Comparative Analysis of Machine Learning Models for Water Quality Prediction 4

Comparative Analysis of Machine Learning Models for Water Qu...

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4th International Conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Krushna, Bode Vamsi Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Water Quality plays an essential role in assessing environmental health, which has profound implications for both ecosystems and human well-being. In this work, various machine learning models including Logistic Regression, Decision Trees, Gaussian Naive Bayes, Random Forest, Support Vector Machine (SVM), and XGBoost were employed to analyze and predict water quality. The dataset used for the analysis encompasses diverse water quality parameters such as hardness, pH levels, solids, chloramines, turbidity, sulfate among others. Class imbalances in the dataset is addressed with Synthetic Minority Over-sampling Technique(SMOTE). The research findings highlight the significant improvement in classification accuracy of underrepresented data points due to SMOTE, with SVM and XGBoost achieving a training accuracy of around 78% and 75%, respectively. The study used K-fold cross-validation techniques and strategies to handle convergence warnings, enhancing the credibility and adaptability of the predictions. This study not only contributes to the growing body of knowledge in water quality analysis but also provides practical insights for researchers and practitioners seeking to employ machine learning approaches in environmental monitoring, water contamination. These findings highlight the critical role of machine learning in facilitating informed decision-making for long-term water management, ensuring the health and longevity of communities and ecosystems. © 2024 IEEE.

关键词： Decision Trees Gaussian Naive Bayes Logistic Regression Machine learning Random Forest SVM Water quality management XGBoost

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Optimizing Smart Contract Security: A Cost-Sensitive Graph Neural Network Approach for Vulnerability Detection

Optimizing Smart Contract Security: A Cost-Sensitive Graph N...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Aravind, M. Saravanan, R. Saranya, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

This paper addresses the issues of blockchain platforms, especially Ethereum, smart contract vulnerabilities have resulted in significant monetary losses. The primary objective of this research is to identify critical smart contract vulnerabilities, such as reentrancy, overflow, and access control problems, by utilizing Graph Neural Networks (GNNs). To evaluate the degree to which two GNN-based models work at reducing practical financial losses, two of them are created using various training approaches. The first model treats each vulnerability class equally in an effort to maximize accuracy. The second model takes a more cost-effective tack by giving training greater weight to major weaknesses. We investigate the trade-offs between accuracy and financial risk reduction using a dataset of 2,217 contracts with imbalanced class distribution, replicating real-world settings. Financial loss estimates are derived from industry-reported incidents, and confusion matrices are employed to demonstrate the detection patterns. The results show that even though the first model's overall accuracy is higher at 96%, it endures more financial loss as a result of missing vital vulnerabilities. With a 94% accuracy rate, on the other hand, the cost-sensitive model effectively lowers high-impact false negatives, resulting in a less overall financial loss. Given that even a slight drop in accuracy might lead to more successful financial risk mitigation, our findings emphasize the significance of prioritizing vulnerability identification based on possible real-world repercussions. The groundwork for incorporating cost-sensitive models into smart contract auditing tools to improve blockchain security is provided by this research. © 2024 IEEE.

关键词： Graph neural networks

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Enhancing Software Test Effort Estimation using Ensemble Learning Algorithms 4

Enhancing Software Test Effort Estimation using Ensemble Lea...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Venkatraman, K. Akashvarma, M. Siddharth, S. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350349900

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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