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Multilingual Sentiment Analysis for YouTube Educational Videos Using NLP And Machine Learning Approaches 3

Multilingual Sentiment Analysis for YouTube Educational Vide...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Martina Jose Mary, M. Shyamala Devi, R. Yogeshkannah, K. Prem, S. Pooranachandiran, G. Viyash, V. IFET College of Engineering Dept of Computer Science Engineering India

ISBN: (纸本)9798350391565

Sentiment analysis has emerged as a critical method for measuring the usefulness of YouTube educational videos in the ever-expanding ecosystem of online educational content. The goal of this project is to create a multilingual sentiment evaluation framework that analyzes and discerns the emotional tenor and attitudes depicted in these movies using machine learning and natural language processing (NLP) techniques. This work intends to provide educators, content providers, and learners with essential insights into the emotional impact of educational materials by constructing a sophisticated model capable of interpreting sentiment across many languages. By using this innovative strategy, we seek to raise the standard of online instruction and create an environment that promotes engaging and successful learning across language and cultural barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Evaluator for Subjective Answers Using Natural Language Processing 7th

Improved Evaluator for Subjective Answers Using Natural Lang...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Sharif, A. Yasin Ravindhar, N.V. Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9783031699818

Examination is a preliminary process for evaluating our knowledge. While objective examinations consist of multiple-choice questions, which are easy to evaluate using simple methods, whereas subjective examinations, which often require written responses, having a significant challenge in uniform evaluation. This type of answer evaluation is a time-consuming process to undertake. This can lead to inconsistencies and biases in grading due to the stress of examiners having to carry out the same task repeatedly. With the technological advancements in the field of Natural Language Processing, which offers a range of techniques that can be employed to evaluate text responses, we have proposed a novel solution for creating an evaluator that increases the accuracy as near to human evaluators compared to other such existing systems for evaluating subjective answers. This model works by taking inputs like the user response, expected sentences and expected keywords and works with the help of numerical vectors, natural language processing and deep learning approaches and other mathematical calculations to calculate the aggregate score for an answer. © IFIP International Federation for Information Processing 2024.

关键词： Deep learning

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Super Resolution of Medical Images Using SRGAN 2

Super Resolution of Medical Images Using SRGAN

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Madhav, S. Nandhika, T.M. Kavitha Devi, M.K. Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9798350328202

Medical imaging is essential for precise diagnosis and treatment planning in contemporary healthcare, yet getting high-resolution pictures can be difficult because of things like low-dose CT scans' reduced radiation exposure. In order to tackle this problem, a unique strategy called Super-Resolution Generative Adversarial Networks (SRGAN) is presented in this research. To improve image resolution, SRGAN uses a generator and discriminator architecture with residual and upscale blocks. By combining adversarial and content losses, the generator's loss function enables it to produce high-quality images while keeping key elements of their low-resolution counterparts. A thorough literature study demonstrates how effective GAN-based models are for the super-resolution of medical images. After 10 training epochs, experimental findings utilizing a Lung CT dataset show a significant improvement, with PSNR and SSIM scores reaching 32.95 and 0.996, respectively. According to these findings, SRGANs are a viable enhancement technique for low-dose CT medical pictures, with significant advantages for precise diagnosis and patient care in healthcare settings. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Telugu Abusive Language Detection Using Word Embeddings and BERT Models 2

Enhancing Telugu Abusive Language Detection Using Word Embed...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Battula, Venkataramana Nune, Nikhil Teja Guptha, Vivek Rampuri, Shiva Sai Chacham, Pradyumna Pyarasani, Koushik Bojji, Anirudh MVSR Engineering College Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

In the digital era, the surge of abusive content online underscores the necessity for robust content moderation. This study targets the challenges posed by abusive content in Telugu, a language with limited digital resources. The goal is an advanced framework to classify Telugu sentences as abusive or not, using FastText and IndicFast for word embedding. Various machine learning techniques like Decision Trees, SVM, KNN, Random Forest, RNN, LSTM, BiLSTM, GRU, and transfer learning with pre-trained BERT models are explored. Each method is evaluated for its ability to capture abusive language nuances. The study employs diverse word vector dimensions for comprehensive coverage, systematically evaluating techniques to identify optimal classification combinations. It also investigates transfer learning via pre-trained BERT models, leveraging knowledge from other datasets to enhance Telugu abusive language understanding. This research enriches natural language processing and content moderation knowledge. It offers insights into effective word embedding techniques and ML algorithms for low-resource languages, aiding in the development of sensitive content moderation tools for Telugu speakers, fostering a safer online space. © 2024 IEEE.

关键词： Random forests

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SafetyNet: A Decentralized Police Complaint Management System for Bangladesh Using Blockchain Technology

SafetyNet: A Decentralized Police Complaint Management Syste...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Tanjim, Shah M.D. Chowdhury, Rahat Ahmed Amin, Ruhul Ahmed, Tanjil Chowdhury, Shovon Das Lysuzzaman, Md Sylhet Engineering College Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350350289

Efficient crime reporting and management is imperative for law enforcement agencies, yet conventional systems pose challenges with data integrity and accessibility. Traditional reporting processes further contribute to delays, errors, and inefficiencies in the overall crime reporting workflow. As a result of these complexities, numerous victims and witnesses are reluctant to seek assistance from legal mechanisms. This study addresses these issues by introducing SafetyNet, a decentralized application (DApp) built on Hyperledger Fabric technology. The prevailing risks of data tampering and unauthorized access are mitigated through Hyperledger Fabric's Private Data Collection, ensuring secure and restricted information storage. SafetyNet is a multifaceted solution comprising user reporting, police, and judge components. Users can seamlessly report crimes, while police and judges access and manage complaints securely within the system. This decentralized approach revolutionizes traditional complaint management, fostering transparency and accountability in law enforcement processes. Hyperledger Fabric emerges as the optimal choice for SafetyNet due to its scalability, permission-based access controls, and confidential transaction processing. This study aims to assess SafetyNet's impact on public trust, compare its effectiveness with traditional systems, and provide valuable insights into blockchain-based complaint management best practices. © 2024 IEEE.

关键词： Distributed ledger

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From Displacement to Employment: Strategies for Sustainable Integration in Host Nations

From Displacement to Employment: Strategies for Sustainable ...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Sawan, K. Patwa, Shreyansh Gupta, Shruti ABES Engineering College Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350376470

Unprecedented global displacement of people due to conflict, persecution and environmental problems requires creative solutions to meet the special educational and employment needs of migrants and refugees. This study examines how digital learning interventions impact displaced people's education and career search results. In our study, we examine the mixed-method experiences of two distinct groups: Group A, which is a control group and receives traditional support, and Group B, which is an experimental group and uses digital learning tools. Demographic profiles, course completion rates, satisfaction levels, and employment status are compared to find patterns and statistically significant differences. Qualitative insights also draw attention to participants' contributions and emerging themes. The findings add to the growing body of knowledge about helping displaced people, shedding light on how digital learning can be the spark that creates better educational and career opportunities. Policymakers, educators, and organizations working to improve the well-being of migrants and refugees worldwide should consider the findings of this study. © 2024 IEEE.

关键词： Digital Learning Displaced Job Recommender

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Enhanced Approach of Face Recognition in Home Security Using Deep Learning Techniques 2

Enhanced Approach of Face Recognition in Home Security Using...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Anandan, D. Sambath Kumar, K.R. Sugadev, P. Srihari, G.A. Surya, B. VSB Engineering College Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

As progressing machine intelligence continues to change differing facets of common history, the integration of AI electronics into home protection systems arises as a hopeful avenue. This paper presents an AI-located home safety solution engaging first recognition science. By leveraging machine intelligence algorithms, the system embellishes usual security measures, contributing a stronger and more efficient wealth of saving residential establishments. Through the reasoning of facial face, bureaucracy can accurately recognize approved things and promptly discover unwarranted access attempts, by helping the safety and solitude of home owners. This paper outlines the design, exercise, and evaluation of the projected AI-located home security arrangement;nephew sizes its influence in checking security risks and providing homeowners accompanying better peace of mind. © 2024 IEEE.

关键词： Deep learning

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Smart Helmet for Riders to Avoid Accidents Using IoT 2

Smart Helmet for Riders to Avoid Accidents Using IoT

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Bhagya Lakshmi, A. Ragunath, C. Yeswanth, Raja Rajkumar, M. Easwari Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

MotorBike accidents are rampant due to the negligence of helmet use, resulting in injuries and fatalities. To address this, a solution emerges in the form of smart helmets with integrated control systems. This project aims to enhance road safety for motorcyclists by preventing the bike from starting unless the rider wears the helmet. The innovation comprises an RF receiving system and RF transmitter. With the persistent surge in motorcycle accidents, the imperative to prioritize safety has led to this technological intervention, potentially saving countless lives. By enforcing helmet usage through a fail-safe mechanism, the smart motorcycle helmet seeks to curb the violation of traffic laws and mitigate the devastating consequences of accidents. The smart motorcycle helmet includes an alcohol sensor to detect intoxication in the rider. If alcohol is detected, a red light on the helmet illuminates, providing a visible indication of impairment. © 2024 IEEE.

关键词： Internet of things

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V- Shaped Bird Algorithm (VSBA) For Energy- Efficient and Reliable Underwater Sensor Network 2

V- Shaped Bird Algorithm (VSBA) For Energy- Efficient and Re...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ayyadurai, M. Harish Rajan, S. Harish, R. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Effective communication and resource management are significantly hampered by the demanding underwater environment. The hard underwater environment causes the limited battery longevity of sensors, Underwater Wireless Sensor Networks (UWSNs) require energy saving, connection, and data reliability. The V-Shaped Bird Algorithm (VSBA) imitates the energy efficient V-formation found in flocks of birds, offering a novel method for Underwater Wireless Sensor Network (UWSN) optimization. By arranging nodes in a V-pattern, VSBA minimizes end-to-end latency and energy usage while reducing communication distances. VSBA extends network lifetime and improves data dependability by balancing energy loads across nodes and increasing packet delivery ratios through strategic node placement. According to simulation data, VSBA performs better regarding reliability and energy efficiency than conventional algorithms like Vector Based Forwarding and Depth Based Routing. Underwater Wireless Sensor Networks (UWSNs), are crucial in multiple fields. Applications for these networks include resource management, surveillance, disaster relief, offshore oil and gas development, and environmental monitoring. © 2024 IEEE.

关键词： Offshore oil well production

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Brain Tumor Diagnosis Using Hybrid Pre-trained CNN-SVM 13

Brain Tumor Diagnosis Using Hybrid Pre-trained CNN-SVM

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Taneja, Pragati Maurya, Rohit Tiwari, Naveen Kumar Chaurasia, Divyansh Yadav, Shashank Computer Science and Engineering Rajkiya Engineering College Kannauj India

ISBN: (纸本)9798350305463

One of the most crucial and challenging jobs in the world of medical imaging is the detection of brain tumors. Manual classification with human assistance can lead to inaccurate predictions and diagnoses. Further, this is a very difficult task when a substantial volume of data must be supported. Brain tumors are highly variable in appearance and similarities between tumors and normal tissue make extraction of tumor regions from the critical image. Early detection of tumors and classifying them, whether cancerous or non- cancerous are very helpful to the patient. We have proposed a hybrid model using pre-trained CNN (Convolutional Neural Network) named as the EfficientNet B3 model and Support Vector Machine(SVM) classifier in this work boosting detection and finally classification of tumor images. There are eight categories of the EfficientNet model i.e. B0-B7. From eight categories, we have used the EfficientNet B3. Because this model gave more accuracy and requires less computation time. In our model, EfficientNet used as feature extractor and SVM used as the classifier. The accuracy of our proposed hybrid model is 99%. The primary objective of this study is to identify categorize tumors into three types: glioma, malignant, and pituitary after correctly identifying them from non-tumor images. © 2024 IEEE.

关键词： Support vector machines

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