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Temporal Harmony: Bridging Gaps in Multivariate Time Series Data with GAN-Transformer Integration 2

Temporal Harmony: Bridging Gaps in Multivariate Time Series ...

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

作者： Saravana, M.K. Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Bangalore University Department of Computer Science and Engineering Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350360523

Multivariate Time Series (MTS) data imputation plays a pivotal role in enhancing the robustness of temporal data analyses across diverse domains. In this paper, we propose a novel hybrid model that combines the generative ability of Generative Adversarial Networks (GANs) with the sequence modeling capabilities of Transformers for MTS data imputation. The generator of the GAN synthesizes realistic MTS data, while the discriminator ensures authenticity by creating an adversarial training dynamic. Simultaneously, a Transformer processes both observed and synthetic MTS data, leveraging its attention mechanisms to capture intricate temporal dependencies and generate imputations for missing values. The joint optimization of the GAN and Transformer components fosters a synergistic model, proficient in generating contextually accurate missing values. We detail the architecture, training process, and hyperparameters of the proposed model, evaluating its performance on diverse MTS datasets. Our model aims to advance the state-of-the-art in MTS data imputation, addressing challenges practically through the fusion of GANs and Transformers. Experimental results showcase promising imputation accuracy and underscore the potential of the hybrid approach in temporal data analytics. © 2024 IEEE.

关键词： Generative adversarial networks

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VQARS: Visual Question Answering for Remote Sensing as a Domain of Choice Incorporating Semantic Intelligence 15th

VQARS: Visual Question Answering for Remote Sensing as a Dom...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Manoj Kumar, H.S. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering PES University Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9783031889912

The need for a visual question answering framework in the era of Web 3.0 is realized by using staged addition of auxiliary knowledge in the perspective of two distinct datasets namely the dataset of documents and the images which is enriched through informative term extraction and further using structural topic modelling, Wikidata, NELL and Linked Open Data (LOD) Cloud, knowledge stores and tag generation to classify the dataset obtained informative terms. Subsequently, the image dataset terms are subjected to extraction of categories and labels to contribute to the informative terms. The random forest classifier at either ends of the two datasets helps in controlled classification of the datasets independently and assures computational inexpensiveness because of the lightweighted nature of the machine learning classifiers. The RNN to classify the tags helps in automatizing the exponentially generated tags and semantic network formulated helps in organizing the auxiliary knowledge. Semantic relevance computation using Jaccard Similarity, Morisitas Overlap Index, NPMI and SOC-PMI facilitates in semantic reasoning. The proposed VQARS model's achieves an overall precision of 94.81%;recall of 95.75%;average accuracy of 95.28%;F-measure of 95.28%;and the lowest FDR of 0.06. In comparison to the other baseline models, this model provides the best-in-class outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Labeled data

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Indian Sign Language Gesture Recognition Using Bi-Directional LSTM and GRU with Text to Speech Conversion 1st

Indian Sign Language Gesture Recognition Using Bi-Directiona...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Santhanam, Pavitra Aashvi George, Sherwin Gopalsamy, Bharathi N. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Campus Chennai India SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9783031823855

This abstract introduces an innovative software system dedicated to recognizing Indian Sign Language (ISL) gestures, leveraging advanced Recurrent Neural Networks (RNNs). ISL plays a pivotal role as a primary communication medium for the deaf and hard-of-hearing community in India. Addressing the critical need for accurate and efficient ISL gesture recognition, our software stands as a pioneering solution, fostering enhanced accessibility and inclusivity. The software meticulously captures the intricate nuances of ISL, facilitated by a meticulously captured dataset comprising of different ranges of Indian Sign Language Gestures. Employing a sophisticated sequence-to-sequence RNN architecture enriched with Long Short-Term Memory (LSTM) components, it adeptly models the intricate temporal dynamics inherent in sign language. This also uses Gated Recurrent Unit(GRU) for processing of the information, and this is also used to overcome the vanishing gradient problem that is very common in RNN This innovative approach empowers the software to achieve unparalleled precision in recognizing ISL gestures, facilitated by comprehensive visual and motion-based data analysis techniques. Text to speech conversion is possible due to playsound library and gTTS library. This also helps in enhancing the project by voicing out loud the names of the gesture accurately. By harnessing cutting-edge technology, our software significantly bridges communication gaps and empowers individuals within the large spectrum of people who have hard to heard symptoms as well as permanent deafness Its accuracy and efficiency not only facilitate smoother interactions but also promote greater societal inclusivity. Thus, our software represents a significant step towards fostering a more accessible and inclusive environment for all members of society, regardless of their hearing abilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gesture recognition

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Advancing Multiclass Emotion Recognition with CNN-RNN Architecture and Illuminating Module for Real-time Precision using Facial Expressions

Advancing Multiclass Emotion Recognition with CNN-RNN Archit...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Prince, Simrith C. Babu, Nirmal Varghese Computer Science And Engineering Karunya Institute Of Technology And Sciences Coimbatore India Data Science And Cyber Security Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Emotions, intrinsic to the human experience, serve as the catalyst for a groundbreaking technological innovation in multiclass emotion recognition through facial expressions. This study introduces a revolutionary system that harnesses advanced deep learning techniques, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to unravel the intricate interplay between emotions and cutting-edge technology. Commencing with a nuanced exploration of the evolutionary origins of emotions, the research lays a robust foundation for understanding the neural mechanisms behind emotional responses. The proposed algorithm unfolds in two pivotal stages: a meticulously designed CNN extracts intricate features from raw facial images, capturing nuanced expressions, while an adaptive RNN establishes temporal dependencies between sequential frames, allowing for a comprehensive understanding of the dynamic nature of emotional states. To address real world challenges, an innovative illuminating module is seamlessly integrated, accounting for contextual factors such as lighting conditions. Empirical evaluations on benchmark datasets underscore the system's exceptional accuracy, achieving an impressive 87.2% accuracy post hyperparameter tuning, surpassing state-of-the-art methods. This transformative method not only precisely captures static facial features but dynamically interprets evolving emotional expressions in real-time, marking a paradigm shift in the field of emotion recognition. Beyond its technical achievements, the proposed system holds profound implications across psychology, human-computer interaction, and market research, positioning itself as a pioneering force in the evolving landscape of emotion-centric technologies. © 2024 IEEE.

关键词： Emotion Recognition

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Image Denoising for an Efficient Fake Image Identification

Image Denoising for an Efficient Fake Image Identification

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Judy Flavia, B. Sharnish, G. Mishra, Prashanth Kohli, Aashirvad Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Noise and blur may be removed from a photograph during picture restoration. Erasing camera shaking, radar imaging, and the influence of an image system's reaction to blurry images is challenging in many instances, including photography. Unwanted signals such as thermal or electrical noise or precipitation or snow in a picture are examples of image noise. The deterioration of the picture might be caused by coding, resolution restriction, transmission noise, object motion, camera shaking, or a combination. A sensor, such as a thermal or electrical signal, or an ambient, such as snow or rain, might cause an image to be blurred. There are several probable causes of picture degradation, including coded pictures, resolution restrictions, transmission noise, object motion, camera shaking, and these factors. Distinguishing between high- and low-frequency components may be accomplished by a process known as "picture decomposition," which entails separating a distorted image into two distinct layers, one for texture and the other for structure, from the LF. The present approach is based on a deep CNN architecture that is very customizable and takes advantage of the frequency characteristics of different sorts of artifacts. The same technique may be used for a wide range of image restoration projects by just altering the architecture. Using a quality improvement network based on residual and recursive learning is recommended to reduce noises with similar frequency characteristics. In order to prevent the network from dying, the authors used residual learning to speed up the training process. The researchers also developed new auxiliary classifi ers. The surtax was applied to the outputs of two inception modules, and an auxiliary loss over the same labels was calculated, much as in the prior experiment. Weighted averages are used to calculate the total loss function. Recursive learning may drastically decrease the number of training parameters while maintaining or increasing pe

关键词： Restoration

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Monitoring Clinical Parameters through Machine Learning and Image Processing

Monitoring Clinical Parameters through Machine Learning and ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Aditya Pai, H. Agarwal, Jyoti Chandrappa, S. Mittal, Tanusha Joshi, Kapil School of Computer Science and Engineering Bengaluru India Computer Science and Engineering Dehradun India Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India

ISBN: (纸本)9798350394412

The purpose of the paper is to find the clinical parameters of an individual using normal webcam. The paper aims to help the healthcare domain. In recent times there has been emergence of smartphone devices which monitor vital information's. These devices have special sensors like Electrocardiogram (ECG) and Photoplethysmography (PPG) to check heart and respiratory rate. The smartphone camera reads the heart and respiratory data from the user's face. The method used in the paper mentions about the variation in the Hue that reads both heart and respiration rate from the face of a user. The result in the paper explains how the proposed approach gives more accurate results than the other detection algorithms. © 2024 IEEE.

关键词： Photoplethysmography

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Speech Recognition Using Machine Learning Techniques 2

Speech Recognition Using Machine Learning Techniques

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mishra, Arya Dhanda, Namrata Gupta, Kapil Kumar Verma, Rajat Amity University Uttar Pradesh Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350371055

With the use of Machine Learning Algorithms, a fundamental task in Human-computer Interaction, Speech Recognition has advanced significantly. The state-of-the-art in machine learning-based voice recognition is summarized in this abstract, which also covers the main challenges, contemporary advancements in the field, and important methodologies. By utilizing automated techniques, voice recognition utilizing machine learning entails translating spoken words into written text or commands. Voice-controlled devices, transcription services, virtual assistants, and other fields have all found use for this technology. The complexity of speech recognition problems has been successfully handled by machine learning techniques such as deep learning models. The abstract concludes by discussing current advancements in voice recognition research, such as end-to-end models, multitask learning, and the incorporation of contextual information from language models to improve the precision and usability of speech recognition systems. In summary, this abstract offers a thorough overview of speech recognition using machine learning, highlighting the developments, difficulties, and potential future directions of this topic that is quickly developing. © 2024 IEEE.

关键词： CNN CTC HMMs NLPs Speech Recognition Traditional Approach

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Cloud Security Analysis using Blockchain technology 2

Cloud Security Analysis using Blockchain Technology

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Meenakshi, K. Bharathi, B. Thangaraj, S. John Justin Sivasubramanian, S. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Cloud computing is now widely employed across a variety of IT-related fields. It has become an essential technology to address infrastructure and data service requirements with minimal effort, low-cost, and with a high degree of scalability. Although the use of cloud computing has increased significantly, information security concerns have not yet been fully resolved. There are still certain information security concerns that must be resolved in order for cloud computing to advance. In terms of integrity, authenticity, and secrecy, in particular, Blockchain has developed into an essential security instrument. The application of Blockchain in cloud computing security is further examined in this article after a review of the different security concerns in blockchain and cloud computing. The main challenge in the cloud computing is cloud computing security. Because data in the cloud must be transported via the Internet, data security becomes a top priority. It is necessary to maintain the primary data protection methods, such as integrity, accountability, privacies, access controls, authentications, and authorizations. Blockchain is a technological advancement that enhances cloud computing. Blockchain resolves cloud computing security problems. This study seeks to analyze and contrast various cloud environment security challenges with blockchain security difficulties. © 2023 IEEE.

关键词： Blockchain

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Improved optimizer with deep learning model for emotion detection and classification

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Mathematical Biosciences and engineering 2024年第7期21卷 6631-6657页

作者： Joseph, Willson C. Kathrine, G. Jaspher Willsie Shanmuganathan, Vimal Sumathi, S. Pelusi, Danilo Valencia, Xiomara Patricia Blanco Verdú, Elena Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kerala India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Sri Eshwar College of Engineering Coimbatore India Communication Sciences University of Teramo Coste Sant’agostino Campus Teramo Italy Universidad Internacional de La Rioja La Rioja Logroño Spain

Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Nonetheless, existing methods are mostly unable to offer correct predictions with a minimum error rate. In this paper, an innovative facial emotion recognition framework, termed extended walrus-based deep learning with Botox feature selection network (EWDL-BFSN), was designed to accurately detect facial emotions. The main goals of the EWDL-BFSN are to identify facial emotions automatically and effectively by choosing the optimal features and adjusting the hyperparameters of the classifier. The gradient wavelet anisotropic filter (GWAF) can be used for image pre-processing in the EWDL-BFSN model. Additionally, SqueezeNet is used to extract significant features. The improved Botox optimization algorithm (IBoA) is then used to choose the best features. Lastly, FER and classification are accomplished through the use of an enhanced optimization-based kernel residual 50 (EK-ResNet50) network. Meanwhile, a nature-inspired metaheuristic, walrus optimization algorithm (WOA) is utilized to pick the hyperparameters of EK-ResNet50 network model. The EWDL-BFSN model was trained and tested with publicly available CK+ and FER-2013 datasets. The Python platform was applied for implementation, and various performance metrics such as accuracy, sensitivity, specificity, and F1-score were analyzed with state-of-the-art methods. The proposed EWDL-BFSN model acquired an overall accuracy of 99.37 and 99.25% for both CK+ and FER-2013 datasets and proved its superiority in predicting facial emotions over state-of-the-art methods. ©2024 the Author(s), licensee AIMS Press.

关键词： Emotion Recognition

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