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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary 2

Analyzing Cyberbullying Word Distribution Across Social Medi...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Sahana, V. Anil Kumar, K.M. Jss Academy of Technical Education Dept. of Information Science and Engineering Bengaluru560060 India Jss Science and Technology University Dept. of Computer Science and Engineering Mysuru570006 India

ISBN: (纸本)9798350372892

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately 84 %. © 2024 IEEE.

关键词： Anonymity

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Investigating the Impact of Chunk Time on Video Streaming Performance in TCP-based Networks 29

Investigating the Impact of Chunk Time on Video Streaming Pe...

引用

29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Chatri, Yeganeh Akbari, Behzad Goudarzi, Pejman Dept. of Computer and Electronic Engineering Tarbiat Modares University Tehran Iran Dept. of Information Technology ICT Research Institute Tehran Iran

ISBN: (纸本)9798331523114

The exponential increase in video traffic on the Internet has generated a heightened demand for efficient video streaming solutions. HTTP Adaptive Streaming (HAS) has emerged as a viable strategy, facilitating dynamic quality adaptation in response to fluctuating network conditions. In HAS, media content is segmented into short time intervals, with each segment encoded in multiple bitrate-resolution combinations known as representations. This framework enables users to choose the most appropriate representation according to their current network connectivity. To enhance the selection of bit rates and elevate the Quality of Experience (QoE), it is crucial to conduct a precise assessment of network conditions. This research investigated the impact of chunk time on video streaming performance in TCP-based networks. Analyzing the effects of chunk time on both QoE and throughput, our findings indicate that similar to the adaptive nature of video representations that depend on network conditions, chunk time must also be adaptive. It is essential that the chunk time is tailored to the specific network environment, necessitating an adaptive chunk-time strategy for each resolution to ensure optimal streaming performance. © 2025 IEEE.

关键词： Frequency division multiple access

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie Database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Breast Cancer Prediction Using Artificial Intelligence technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining Machine Learning

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Comparative Study on Serial and Parallel Implementation of Face Detection 2

Comparative Study on Serial and Parallel Implementation of F...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Mallegowda, M. Theertha, K. Varsha, S.D. Kanavalli, Anita Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Prof. and Dept of CSE. Bangalore India

ISBN: (纸本)9798350306415

Large-data set artificial neural network training takes a lot of time. Many ways for reducing effort have been proposed, many of which make use of parallelization techniques. This paper explores the implementation of face detection algorithms utilizing OPENMP to achieve greater efficiency through parallelization. We focus on specific OpenMP parallelization setups that run on a typical multi-threaded CPU. These frameworks are also available for CUDA, however utilizing CUDA is only possible if you have an NVIDIA graphics card, which is clearly not the case for everyone. OpenMP's release of a stable version in late 2015 facilitated parallel processing and broadened its development base. © 2024 IEEE.

关键词： face detection algorithms OpenMP parallelization

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Automated segmentation and classification of the atherosclerotic carotid plaque in ultrasound videos 24

Automated segmentation and classification of the atheroscler...

引用

24th International Conference on Digital Signal Processing, DSP 2023

作者： Liapi, Georgia D. Gemenaris, Michalis Loizou, Christos P. Kyriacou, Efthyvoulos Constatninou, Kyriacos P. Nicolaides, Andrew Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Dept. of Computer Science University of Cyprus Nicosia Cyprus Vascular Screening and Diagnostic Centre Nicosia Cyprus

ISBN: (纸本)9798350339598

The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selection of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selected per CUS video, and the CC's consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selection. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p © 2023 IEEE.

关键词： Frequency modulation

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Evaluating Large Language Models for Code Generation: Assessing Accuracy, Quality, and Performance 2

Evaluating Large Language Models for Code Generation: Assess...

引用

2nd International Conference on Foundation and Large Language Models, FLLM 2024

作者： Shehab, Mohammed A. Wardat, Mohammad Omari, Safwan Jararweh, Yaser Concordia University Dept. Concordia Continuing Education Montreal Canada Oakland University Dept. of Computer Science and Engineering Rochester Hills United States Lewis University Dept. of ECaMS Romeoville United States Jordan University of Science and Technology Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350354799

Large Language Models (LLMs) are increasingly utilized for software engineering tasks, including code generation. While prior research has primarily focused on code completion, there remains a gap in understanding LLMs' ability to generate code from Natural Language Processing (NLP) descriptions. In this study, we investigated three LLMs' performance in generating code from scratch based on NLP task descriptions. We employed three evaluation levels, Accuracy, Quality, and Performance, to assess the LLMs' results. Accuracy metrics focused on error types and counts, quality assessed code readability and maintainability, and performance measured the models' ability to generate optimized solutions. Codex, Copilot, and PaLM2 exhibited error rates of 9.68%, 32.25%, and 48.38%, respectively. Copilot showed the best performance with a higher maintainability index. However, all LLMs struggled with tasks of high complexity, with time complexities of O(m•n•log(n)), as evidenced by their performance metrics. © 2024 IEEE.

关键词： Software quality

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System 2

Leveraging Artificial Intelligence for Enhanced Data Generat...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Bandopadhyay, Srijita Dutta, Srimonti Haider, Imran Anuraag, Bhavaraju Zhu, Jerry Bazaz, Saad Ahmed University of Engg. & Management Dept. of Electronics and Communication Engineering Kolkata India Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India Vellore Institute of Technology Dept. of Electronics and Communication Engineering Vellore India University of Waterloo Faculty of Mathematics ON Canada National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350360523

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the ß- VAE filtered approach employed in previous literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Using textile capacitive sensors to train synchronized movement of hands

Using textile capacitive sensors to train synchronized movem...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Chen, Shih-Hung Wu, Ze-We Liao, Kuo-Cheng Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This study introduced a capacitive sensing interactive game platform aimed at promoting emotional stability, which we have named the 'Sunrise and Sunset' game. This game primarily consists of two pieces of regular textile fabric enveloping conductive silver fabric. A microcontroller was employed to extract the sensed capacitive values, and a game named 'Sunrise and Sunset' is designed to complement the slow raising and lowering of both hands. The development of this gaming platform has the potential to offer a novel method of emotional management, particularly in high-stress living environments. It can serve as an effective relaxation tool, aiding individuals in emotional balance, anxiety reduction, and stress alleviation. Simultaneously, this platform can contribute to the promotion of mental well-being, providing an engaging and beneficial means for people to manage their emotions and moods. © 2024 IEEE.

关键词： capacitive sensing emotional stability game relaxation tool silver fabric

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