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Voice Assisted Facial Emotion Recognition System For Blind Peoples With Tensorflow Model

Voice Assisted Facial Emotion Recognition System For Blind P...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Ashmad Ahemed, S. Jayakumar, D. Ifet College of Engineering Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350348460

This research project presents the development of a novel Voice-Assisted Facial Emotion Recognition System tailored to address the unique needs of blind individuals. Leveraging the power of deep learning and the TensorFlow framework, the system employs computer vision techniques to detect and interpret facial emotions, enabling blind users to comprehend the emotional cues of those they interact with. The project encompasses data collection, preprocessing, model training, and evaluation, resulting in a robust TensorFlow model capable of real-time emotion recognition from facial images. A voice assistant integrates seamlessly into the system, offering spoken feedback to users, thus enhancing their understanding of emotional contexts during social interactions. The system prioritizes user accessibility and privacy, ensuring a user-friendly interface and adherence to data security standards. Extensive testing and user feedback are essential components of the project, allowing for continuous improvement and optimization to meet the specific needs of blind individuals. This research offers a promising solution to empower the visually impaired community by providing them with a valuable tool for enhanced social interaction and emotional understanding. Real-World Application, Emphasize the practicality and real-world applicability of the system, showcasing how it can make a meaningful impact on the daily lives of blind individuals by helping them navigate social and emotional contexts more effectively. Multimodal Interaction, Highlight the multimodal nature of the system, which combines visual facial emotion recognition with voice-based interaction to provide a comprehensive and accessible user experience. © 2024 IEEE.

关键词： Convolutional Neural Network Emotion identification Tensorflow

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Dehazing of Multispectral Images Using Contrastive Learning In CycleGAN 5th

Dehazing of Multispectral Images Using Contrastive Learning ...

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5th International Conference on Data and Information sciences, ICDIS 2023

作者： Kayalvizhi, S. Karthikeyan, Badrinath Sathvik, Canchibalaji Gautham, Chadalavada Computer Science and Engineering Easwari Engineering College Chennai Chennai India

ISBN: (纸本)9789819969050

Picture dehazing is a necessary aspect in computer vision, especially when employed with surveillance and satellite data. In this study, we provide a unique descriptive learning method for multispectral image augmentation based on CycleGAN. The suggested technique enhances the efficiency of the dehazing model by utilizing the benefits of descriptive learning and CycleGAN. Contrastive learning is used to separate hazy and clear images, while CycleGAN is used to transform hazy images into their dehazed counterparts. The proposed system is trained and evaluated on the RESIDE dataset, a benchmark dataset for image dehazing. The experiments demonstrate that the proposed system outperforms several existing methods, including CNN, DCP, HOT, DehazeNet, AODNET, and GDN. Our proposed system has practical implications for applications such as remote sensing and surveillance, where accurate image dehazing is essential. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Remote sensing

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Understanding Global Warming: An In-depth Analysis and Future Predictions 5

Understanding Global Warming: An In-depth Analysis and Futur...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Umar Mohammed, E. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

This research conducts a comprehensive analysis of global warming trends by integrating Earth surface temperature, CO2 emissions, and world population data. Applying sophisticated machine learning models (SARIMAX, ARIMAX, VAR, VECM), the study uncovers complex interplays between these variables and provides accurate future trend predictions. The data collection and preprocessing ensure reliability, while the analysis of Earth surface temperature identifies the patterns. The investigation into the relationship between CO2 emissions and climate dynamics reveals the impact of human activities. Exploring the correlation between world population growth and environmental repercussions, Machine learning models scrutinize population trends. Predictive modeling using SARIMAX, ARIMAX, VAR, and VECM forecasts future trends, validated for accuracy. Conclusions emphasize the unique contributions of each model, discussing implications for climate change mitigation. Future research should focus on recommendations that aim to refine predictions, contributing valuable insights to the ongoing dialogue on global warming for evidence-based decision-making toward a sustainable future. © 2024 IEEE.

关键词： ARIMAX correlation interplays mitigation predictions preprocessing SARIMAX VAR VECM

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The Role of Metaverse Technology in Education: A Framework for Implementation and Future Research

The Role of Metaverse Technology in Education: A Framework f...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Sineka, P. Jothikumar, Karthikeyan Nivetha, V.M. Luckshika, M.K. Priyadharshini, P. Janani Ratthna, S.G. National Engineering College Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350374711

In this article, our study aims to pioneer a transformative approach to online education by creating a Metaverse college environment. In this immersive 3D space, avatars fluidly represent both teachers and students, fundamentally altering the dynamics of virtual learning. Lectures transcend traditional boundaries through VR presentations, enriching engagement and understanding. Interactive quizzes inject a gamified element into assessments, fostering a dynamic and enjoyable learning journey. One of the standout features is the use of VR glasses for attendance tracking, ensuring precise reflection of real-world participation. This innovative fusion of cutting-edge technology and traditional teaching methodologies heralds a paradigm shift in remote education. Our platform is designed to provide students with an authentic college experience within the digital realm, fundamentally reshaping the landscape of virtual learning. By seamlessly integrating advanced technologies with tried-and-true educational practices, we aim to create an environment where students thrive and actively engage with course materials. This Metaverse college experience promises not only to enhance learning outcomes but also to redefine the possibilities of online education. Join us on this journey as we revolutionize the way we learn and teach in the digital age. © 2024 IEEE.

关键词： Virtual environments

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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Image to Text Conversion Using Deep Learning Algorithms: Survey 10

Image to Text Conversion Using Deep Learning Algorithms: Sur...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Saranya, M. Anusuya, V. Veera Santhiya, R. Vazhan Arul Srimathi, R. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350384369

Image Captioning (ICs) seamlessly combines the realms of computer Vision (CV) and Natural Language Processing (NLP) task involved in producing textual sentences that summarise the image content in a way which is understandable to humans. The primary objective is to enable machines to convey the content of images in human-readable language. Image understanding and text formation are typically the two primary processes in the image captioning process. A family of Convolutional Neural Network (CNN) is used to retrieve visual information from an input image during the image understanding process. This visual information includes the image's scenes, relationships, and objects. To translate these visual features into coherent and contextually appropriate captions, recurrent neural network, long short-term memory, transformer-based models, etc. are used in the text generation step. In this article, we propose a survey about the previous research paper and the fundamental steps involved in image captioning tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Node to node communication security In IoT networks with hybrid cryptography and steganography system

Node to node communication security In IoT networks with hyb...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sreevidya, Medam Venkata Ramyadevi, R. Chamundeeswari, V. Vijaya Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

The Internet of Things refers to actual objects or items that have been integrated with sensors, algorithms, and a few other technologies to exchange information with other devices and networks online (IoT). As IOT devices are publicly available to everyone on the network, it is required to be conscious of security risks and dangers posed by cyberattacks;as a result, it should be guarded. Plain information is changed to encrypted information in cryptography before being delivered, and it is then converted back to plain text after receiving a response from the recipient. Steganography is the practice of encoding hidden information into an audio, video, picture, or text file. In this project, it is suggested that IoT network data be encrypted using cryptography, and that the encrypted message be concealed somewhere in a digital image through steganography. It is also suggested to enhance the number of bits that can be stored within a single picture pixel. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Cryptography

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Early Breast Cancer Prediction Using Machine-Learning Algorithms

Early Breast Cancer Prediction Using Machine-Learning Algori...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Abirami, S. Sri Vignesh, R.B. Srihariprasath, V. Mathialahan, U. Yaswanth, T.S. Computer Science And Engineering Sri Krishna College Of Engineering And Technology India

ISBN: (纸本)9798350364828

Breast cancer poses a significant global health challenge, affecting millions of women each year and leading to substantial cancer-related deaths. Early detection is crucial, particularly in regions with limited resources and inefficient healthcare systems where late-stage diagnoses are common. To tackle this issue, we propose a novel approach that employs machine-learning algorithms for early breast cancer prediction. Our model integrates a diverse ensemble of machine-learning algorithms, including K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), and Decision Tree Classifier. This ensemble harnesses the collective predictive power of these algorithms to distinguish between benign and malignant tumor types, enabling early diagnosis and prompt intervention. Furthermore, our approach enhances predictive capabilities by incorporating Logistic Regression, Random Forest, and additional KNN algorithms. Each algorithm contributes unique strengths to the ensemble: SVM excels in identifying complex data relationships, Logistic Regression offers interpretability, Random Forest handles large datasets and captures intricate patterns, while KNN adapts well to irregular decision boundaries. By leveraging the strengths of these diverse algorithms, our proposed system provides a more comprehensive and accurate breast cancer prediction model. Through ensemble learning, we mitigate individual algorithm weaknesses and improve predictive performance, thereby advancing the effectiveness of early breast cancer detection and ultimately improving patient outcomes. Keywords: Machine Learning, Breast Cancer Prediction, Ensemble Learning, K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, Early Detection, Healthcare Systems © 2024 IEEE.

关键词： Learning systems

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Skill measuring test for preschoolers

Skill measuring test for preschoolers

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Nagappan, G. Narmatha, K. Nithiyasri, K.M. Sai Gayatri, S. Department of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9781032669663

Psychometric evaluation evaluates the information, abilities, capacity and persona trends of someone being assessed. Since many youngsters are joined in faculty earlier than they meet the specified age criteria, they lack the fundamental knowledge and information. This creates a susceptible base for them of their scholar life. In order to check their capacity to get promoted to better grade this psychometric check might be beneficial to get evaluation of the child’s intelligence. This check consists of five kinds of intelligence. They are Verbal or Linguistic Intelligence, Mathematical/Logistic Intelligence, Interpersonal Intelligence, Intrapersonal Intelligence, Naturalistic Intelligence. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Interpersonal intelligence intrapersonal intelligence Linguistic intelligence Logical intelligence Logistic Intelligence Multiple intelligence Naturalistic intelligence Verbal Intelligence

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A Novel Approach for Classification of Crops and Weeds using Deep Learning

A Novel Approach for Classification of Crops and Weeds using...

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

作者： Ram Prakash, L. Pravinesh, H. Ariyamala, V. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

Weed detection is very important in modern agriculture, and timely and fast identification of weeds helps to reduce crop losses that have a positive effect on the yield. The classical methods of weed management involve manual weeding or broadcast spraying with herbicide, and both have laborious, expensive, and dismaying effects on the environment. The demand has never been higher for automatic weed detection systems in agriculture. Abstract: This project aims to build an automated weed detection system with YOLOv8, one of the most state-of-the-art object detection algorithms that excels in real-time accuracy. The system utilizes high-resolution drone and camera images of fields to pinpoint the specific plants growing in a crop, enabling more selective herbicide application and minimizing the need for manual labor from cropping operators. The design of YOLOv8 ensures high-speed and high-precision performance, making it ideal for use as a real-time detection system in the field. We train our model on a home-grown dataset of approximately 8k crop and weed images (gm), incorporating additional data augmentation for enhanced robustness. We will assess performance using evaluation metrics like precision, recall, F1-score, and mean average precision (mAP). © 2024 IEEE.

关键词： Deep learning

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