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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

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Deep learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

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Detection of Student Impulse in the Classroom UsingFacial Emotion Recognition 12

Detection of Student Impulse in the Classroom UsingFacial Em...

引用

12th international conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Chatterjee, Debajyoti Sur, Surajit Bhattacharyya, Kanjari Department of CSE University of Engineering and Management Jaipur India Department of ECE University of Engineering and Management Jaipur India

ISBN: (纸本)9798350387315

Emotions play a vital role in students' academic performance and overall well-being. However, detecting and understanding students' emotions remains a challenging task for educators. This study proposes a facial emotion recognition (FER) system to detect and analyse students' emotions in real-time, providing valuable insights for teachers to create a more supportive learning environment. Our FER model utilizes deep learning techniques to classify facial expressions into six basic emotions (happiness, sadness, anger, fear, surprise, and neutral). We collected a dataset of facial images from students during lectures and evaluated our model's performance. This research contributes to the development of affective computing in education, offering a non-invasive and objective emotion detection method. Our findings have implications for educators, policymakers, and researchers seeking to create emotionally intelligent learning environments. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

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The Prediction of Mental Health Using Machine learning 15

The Prediction of Mental Health Using Machine Learning

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhawale, Hrutuja Thakare, Dhanashree Morris, Nekita Chavan Agrawal, Rahul Dhule, Chetan G.H Raisoni College of Engineering Department of Data Science IOT Cyber Security Nagpur India

ISBN: (纸本)9798350370249

Depression is the mental health condition that affects many people around the world. In order to find and treat depression and other mental health conditions, it is important to find those who are at risk so that they can be helped as soon as possible. The early diagnosis and prevention have been the help of the machine learning techniques to the prediction of depression. The different machine learning models and datasets used, and the current literature on depression prediction have been thoroughly inspected. In the project, we gathered data from employees and visualized it using KNN algorithm. Finally, the layout of the heatmap, the confusion matrix, pie chart, bar plots and the forest of the data was drawn. After that, we used KNN and logistic regression to the data and the accuracy was 0.825 and 0.73333, respectively. The KNN algorithm was more accurate, i.e. the number was 0.825 (82. 5%). © 2024 IEEE.

关键词： Health Data Clustering Health Data Visualization K-Nearest Neighbors (KNN) Logistic Regression Machine learning in Healthcare Mental Health Predictive Modeling in Psychology

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Brain Tumor Segmentation Using Semi-supervised learning 15

Brain Tumor Segmentation Using Semi-supervised Learning

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shetgaonkar, Pratiksha Koli, Aakash Mandrekar, Pushpam Silveira, Fraser Dessai, Sukanya S.R.I.E.T. Dept. Of Computer Engineering Goa Shiroda India

ISBN: (纸本)9798350370249

Accurate segmentation of brain tumors is crucial for diagnosis and treatment planning in neuroimaging. Using labeled and unlabeled data and leveraging semi-supervised learning approaches can enhance segmentation performance. This work proposes a novel methodology for semi-supervised brain tumor segmentation, employing context restoration for pseudo-label generation as a pretext task. We investigate the efficacy of this approach across three prominent variants of the U-Net architecture: the standard U-Net, Attention U-Net, and U-Net++. By varying the proportion of labeled and unlabeled data, we conduct a comprehensive comparative analysis to evaluate the segmentation performance of each model. The results display the effectiveness of the proposed method in enhancing segmentation accuracy, particularly in scenarios with limited labeled data. Furthermore, we provide insights into the relative performance of different U -Net variants under varying data availability, offering valuable guidance for practitioners in selecting an appropriate model for semi-supervised brain tumor segmentation tasks. © 2024 IEEE.

关键词： Attention U-Net Brain Tumor Segmentation Deep learning Semi-Supervised learning U-Net++

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Spatial attention enhanced deep learning model for Alzheimer's disease diagnosis 15

Spatial attention enhanced deep learning model for Alzheimer...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Upendra Kumar, Vidit Pant, Bhaskar Graphic Era deemed to be university Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350370249

Diagnosing Alzheimer's disease has a major challenge for neurologists due to the time-consuming and sometimes imprecise manual methods. Given Alzheimer's substantial impact on the brain, employing an automatic diagnostic system using brain images could yield more precise outcomes. In this work, a spatial attention assisted improved deep network model is proposed for classifying Alzheimer's disease with brain magnetic resonance images. Using ADNI2 dataset, the proposed model demonstrates commendable performance, achieving a accuracy rate of 86% (AD vs MCI vs CN), which is better than previous methods. © 2024 IEEE.

关键词： Alzheimer's disease (AD) brain imaging deep learning (DL) Medical image analysis Spatial Attention

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Bangla Sign Language Classification with Deep learning 6

Bangla Sign Language Classification with Deep Learning

引用

6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Ahmed, Md. Kawsar Salam, Tonima Pinky, Lubna Yasmin Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh

ISBN: (纸本)9798350385779

BdSL (Bangla Sign Language) is a language in its own right, with its own grammar, syntax, and rules for word order. There are 36 alphabets in the Bangla sign language (BdSL), some of which have pretty similar characters. In our study, we use a convolutional neural network (CNN) method combined with Image Processing to classify Bangla sign alphabets and digits. We evaluated the performance of our scratched CNN model to pre-trained VGG-16 and model using the transfer learning method using our self-created dataset and IsharaLipi digits dataset. Our proposed model predicts digits with 99.38% accuracy and letters with 97% accuracy. In comparison to existing pre-trained models, our model needs extremely less computing resources and produces considerably superior accuracy outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Cloud Virtualization Security with a Z-Score Based Conjugate Self-Organizing Migration ML Model 15

Enhancing Cloud Virtualization Security with a Z-Score Based...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sidana, Sagar Thakur, Sujata Negi Sinha, Anurag Indhumathi, N. Raj, Petheru Al-Khayyat, Ahmed Modi, Vyom Mishra, Aditya Kumari, Anita Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India Graphic Era Hill University Department of computer science and engineering India Ignou School of Computing and information Science New Delhi India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Marwari Education Foundations Group Of Institutions Rajkot India Gla University Department of computer science and engineering Applications Mathura India Rkdf University Department f English Literature India

ISBN: (纸本)9798350370249

This study presents a new machine learning model for detecting malware in cloud computing settings called Conjugate Self-Organizing Migration (CSOM), which is based on Z-score. Feature vectors are standardized by the model through the use of Z-score normalization, which improves its flexibility with changing data distributions. By merging self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the threat of growing malware. Using Z-score normalization to guarantee correct representation of changing data, the imaging approach depicts the cloud environment as a dynamic, self-organizing landscape. Through conjugate migration techniques, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of tiny fluctuations in harmful actions. The Z-score-based CSOM model is effective in detecting malware at high accuracy rates in a variety of dynamic cloud environments, as demonstrated by the experimental results. The model demonstrates its flexibility in responding to changing cyberthreats with its strong performance in real-world settings. Finally, with its better accuracy and adaptability, the suggested Z-score-based CSOM model offers a potential approach for cloud computing virus detection. In order to handle the constantly shifting environment of cloud-based cyber threats, further research may examine scalability and more refinement. © 2024 IEEE.

关键词： Cloud computing Conjugate Self-Organizing Migration Machine learning Model Malware Detection Z-score

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Leveraging Deep learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing the Handwritten Digit Recognition by Employing Novel Progressive VGG19 Model and Compare with SVM Performance 15

Enhancing the Handwritten Digit Recognition by Employing Nov...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Harish Kumar, G. Jaisharma, K. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept of CSE Chennai India

ISBN: (纸本)9798350370249

This research aims to predict handwritten digits using the innovative Novel Progressive VGG19 algorithm, contrasting the SVM performance. The Novel Progressive VGG19 is applied to a sample size of 74 sets, while the SVM algorithm is utilized with a sample mean size of 74 sets, totaling 148 sets for comprehensive comparison and heightened research accuracy. Mean accuracy is computed using ClinCalc software in supervised learning, adopting an alpha value of 0.5, a G-Power value of 80%, and a 95% confidence interval. The Novel Progressive VGG19 achieves an accuracy of 74.65%, surpassing the SVM algorithm's accuracy of 52.32%. An Independent Samples T-Test indicates a significant p-value of 0.000 (p( © 2024 IEEE.

关键词： Contrastive learning

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Stochastic Large-scale Machine learning Algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Fang, Biyi Klabjan, Diego Vo, Truong Northwestern University Dept. of Engineering and Applied Mathematics EvanstonIL60208 United States Northwestern University Dept. of Industrial Engineering and Management Sciences EvanstonIL60208 United States Northwestern University Dept. of Machine Learning and Data Science EvanstonIL60208 United States

ISBN: (纸本)9798350362480

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm. © 2024 IEEE.

关键词： Contrastive learning

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