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A Review on Amalgamation of Textile-based Components for Standardization of Smart Fabrics 5

A Review on Amalgamation of Textile-based Components for Sta...

引用

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

作者： Kumar, Sandeep Haripriya Taneja, Neeti Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

This article provides an overview of the fundamental ideas and methodologies utilized in the construction of fabric sensors, along with the frequently employed materials and procedures in the field of electronic textiles. It illustrates that depending on the level of integration within the fabric platform, sensing capabilities may be accomplished by either intrinsic or external modifications to textile substrates. The present study showcases the adaptability of fabric sensors in quantifying force, pressure, chemicals, humidity, and temperature fluctuations. The materials, connections, fabric circuits, interconnect, encapsulation, and manufacturing processes used in fabric technologies are very adaptable and diverse. However, they are not as durable as the components used in traditional electronics. The survey results indicate that it is feasible to create a comprehensive smart fabric system by combining various types of functional elements based on textiles. This effort aims to initiate the standardization of smart fabric sensing techniques and e-textile production procedures. © 2024 IEEE.

关键词： e-textiles Fabric sensors textile sensors

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A Novel Approach for Lightweight Sign Language Recognition Leveraging Google Mediapipe and Deep Neural Net 1

A Novel Approach for Lightweight Sign Language Recognition L...

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Siju, Ijas Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350352931

The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seamless communication between individuals and between humans and machines through Interfaces. However, existing SLR systems pose significant challenges, including the demand for high accuracy, low latency, simplicity in development and deployment, lightweight design for edge compatibility, and efficient resource utilization during training. Addressing these challenges necessitates developing a system that balances training data accuracy, ease of training, and resource efficiency. Conventional Deep learning approaches often rely on high-dimensional image datasets for training HGR models, resulting in trade-offs between latency, accuracy, complexity, and resource requirements. To mitigate these challenges, this paper harnesses the capabilities of Google Mediapipe to extract a 21-point hand landmark vector from each training image. By leveraging this low-dimensional data, we train model that are accurate, lightweight, less complex, and exhibit low latency, making them compatible with edge devices. The hand gesture images were collected for several gestures, such as peace, okay, and stop. These images will be converted to landmark vectors using Mediapipe. These landmark points were used to train the Deep Neural Network architecture developed in Tensorflow. The model was trained with each hand gesture assigned with a common word in English and the proposed model was tested with a live webcam stream. The proposed model is developed in Python in the Google Colab IDE with the help of libraries and frameworks like OpenCV, Tensorflow and Google Mediapipe. The proposed model outperforms current state-of-the-art approaches by achieving an accuracy of 9 4. 8 8 %. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Driven Health: A Web App for Enhanced Healthcare Queries and Nutrition Analysis 5

AI-Driven Health: A Web App for Enhanced Healthcare Queries ...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ushashree, P. Naik, Archana Sri, P. Amrutha Sai Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331504403

The AI-Driven Health chat assistant is an innovative healthcare solution that seamlessly integrates technology and care, enabling users to have natural language conversations about symptoms, treatments, and general health inquiries. The chatbot functions as a virtual assistant, enabling users to inquire about medical concerns and obtain clear explanations. The nutrition bot utilizes the advanced capabilities of Google Gemini to analyze food images, offering users comprehensive nutritional information. The proposed system undergoes constant refinement through regular testing and user feedback, guaranteeing a smooth and dependable platform for accessible healthcare interactions. The AI-Driven Health chat assistant strives to enhance the accessibility and comprehensibility of healthcare information, ultimately enhancing a healthier and more informed society. © 2024 IEEE.

关键词： Bot (Internet)

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Identifying Authentic Reviews on Amazon Dataset using Transformer-Based Models

Identifying Authentic Reviews on Amazon Dataset using Transf...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Azam, Hadiqa Chaudhry, Hassan Nazeer Majid, Muhammad Computer Department University of Engineering and Technology Taxila Pakistan Computer Science Department Barani Institute of Information Technology Rawalpindi Pakistan

ISBN: (纸本)9798331504465

Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-based model that utilizes GloVe (Global Vectors for Word Representation) semantic embeddings to detect fraudulent reviews. Our approach captures subtle linguistic patterns indicative of fake reviews by fine-tuning a pre-trained GloVe model on a labelled dataset. The GloVe embeddings improve the model’s grasp of word semantics, while an attention-based neural network classifies reviews as genuine or fraudulent. Our model has surpassed traditional machine learning methods, achieving 98% accuracy across various datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Elastic Stack: A Comprehensive Overview 6

Elastic Stack: A Comprehensive Overview

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Shaji, Ayda Sara George, Mithu Mary Toc H Institute of Science and Technology Department of Computer Science and Engineering Kerala India Muthoot Institute of Technology and Science Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This document thoroughly explores the Elastic Stack, comprising Elasticsearch, Logstash, Kibana, and Beats, emphasizing its effectiveness in real-time data analysis, log handling, and visualization. It examines the essential components, architecture, and practical uses in different fields, offering in-depth technical insights into each element, integration methods, and practical examples. Readers will gain a comprehensive grasp of the Elastic Stack's capabilities and its application potential in various scenarios by the conclusion of this paper. © 2024 IEEE.

关键词： Data handling

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Motion Detection in Real-Time Surveillance Using Two Frame Differencing 1

引用

8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan Mandal, Gouranga National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India Vellore Institute of Technology Andhra Pradesh India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

This research article delves into the creation of a real-time system for detecting motion, particularly for surveillance purposes. The proposed approach relies on computer vision algorithms to analyze live video streams captured by a camera. By scrutinizing these feeds, the algorithms can discern alterations within the scene, specifically identifying objects in motion. The primary aim is to ensure that this system functions in real time, thereby furnishing instantaneous insights to security personnel. The paper expounds on the different steps integral to motion detection, encompassing procedures like background subtraction, foreground segmentation, and object tracking. To validate the system’s effectiveness, an evaluation is conducted using a collection of actual surveillance videos. The findings underscore the system’s capability to accurately detect moving objects across diverse scenarios. The article concludes by contemplating the system’s potential applications, which span security setups, traffic supervision, and ecological monitoring. The focal technique utilized in this study is Frame Differencing, a means of detecting object motion. This approach is adept at distinguishing moving objects within a given environment. In conjunction with this method, the paper emphasizes the viability of background subtraction to further enhance Frame Differencing, thereby augmenting its precision and efficacy. Upon applying this method to a camera, it is discerned that luminosity has a significant impact on the minimal value. Notably, a minimal value of 35 is identified as the most favorable choice. Here, threshold value 35 is selected because we have seen in our experiment that 35 gives the optimal result with our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cameras

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. Therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. The proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). The dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. This structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 IEEE.

关键词： Support vector machines

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