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Augmented Reality and Artificial intelligence in Sign Language Expression 3

Augmented Reality and Artificial Intelligence in Sign Langua...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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A Real-time Visual Target Detection and Automatic Tracking Algorithm Based on YOLOv3 and DWSiamRPN 3

A Real-time Visual Target Detection and Automatic Tracking A...

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3rd international conference on pattern recognition and machine Learning, PRML 2022

作者： Zhong, Yexiu Qiu, Senhui Zhao, Zhipeng He, Fuyun Guilin Institute of Information Technology School of Electronic Engineering Guilin China Guangxi Normal University School of Electronic Engineering Guilin China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The current single-target trackers cannot detect the categories of the targets, and multi-object trackers have low performances due to the missed and false detections. Most of these trackers do not have the capabilities for the interesting target selections. To overcome these problems, a real-time visual target detection and automatic tracking algorithm is proposed. First, the video image is detected by using YOLOv3. Then the interesting target is obtained by the proposed intelligent selection strategy. Finally, the selected target is tracked in real-time by the Deeper and Wider Siamese Networks (DWSiamRPN). Experiments show that the proposed YOLOv3-DWSiamRPN method can effectively detect, automatically obtain and track targets in real-time (26 frames per second), with 55.5 mean Average Precision (mAP) on COCO dataset, 0.296 Expect Average Overlaprate (EAO) on VOT-2017 and 0.66 Area Under Curve (AUC) on OTB-2015, respectively. Furthermore, it also addresses the problems of short-term and long-term target losses. © 2022 IEEE.

关键词： Target tracking

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CAM: Compound attention module for improved vision models 3

CAM: Compound attention module for improved vision models

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2024 3rd international conference on Image Processing, Object Detection, and Tracking, IPODT 2024

作者： Liu, Houhong Tan, Fei Du, Zhengmiao School of Automation and Information Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Key Laboratory of Artificial Intelligence Yibin China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In recent years, the attention mechanism has played a significant role in enhancing algorithm performance in deep learning-based visual tasks. Most methods focus on developing more complex attention mechanisms to improve network performance, which inevitably increases the computational complexity of the model. To balance performance and computational complexity, this paper proposes the CAM (Compound Attention Module) attention mechanism, which delivers substantial performance improvements with only a slight increase in parameters. The CAM module operates in two dimensions: space and channel. It is a lightweight, plug-and-play module with minimal computational overhead. We validate our CAM module through extensive experiments on image classification and object detection tasks using the CIFAR-100 and VisDrone2019 datasets. Experimental results demonstrate that the model consistently improves image classification and object detection performance, outperforming similar modules. © 2024 SPIE.

关键词： machine vision

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft Computing Paradigms for Load Balancing in Cloud Computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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A Review of Facial Video Analysis for Heart Rate Estimation Using Artificial intelligence 3rd

A Review of Facial Video Analysis for Heart Rate Estimation ...

引用

3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Toley, Minal Chandrakant Mishra, Raj Gaurav Shirsath, Vishal School of Engineering Ajeenkya D Y Patil University Maharashtra Pune India

ISBN: (纸本)9789819743582

The most significant indicator of cardiovascular disease is a rapid heart rate. Traditional methods for detecting heart rate include the use of Electrocardiogram (ECG) and oximeter sensors. However, ECG requires skin contact with gel patches, which can be irritating for patients. Conversely, when pulse oximeter sensors are applied to the fingertip or earlobes for extended periods, they may lead to discomfort. Heart rate serves as a critical physiological metric for assessing heart function and overall physical health. It also plays a vital role as a medical indicator for diagnosing cardiovascular disorders and certain chronic conditions. The number of heart beats per minute, commonly known as heart rate, is an important physiological indicator that can reveal psychological and physical conditions. Remote vital sign sensing employs cam- eras instead of skin-contact sensors to provide improved access to measuring conditions. This approach relies on the principles of color and motion. Remote photoplethysmography (rPPG) examines changes in the tone of the skin layer of the face due to blood circulation, while remote ballistocardiography (rBCG) analyzes the minute head movements induced by the heartbeat. In this study, we present a machine learning strategy aimed at enhancing the accuracy of heart rate detection in naturalistic measurements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Oximeters

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Islanding Detection Using pattern recognition by HOG Features with Ensemble Classifier 3

Islanding Detection Using Pattern Recognition by HOG Feature...

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3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Roy, Soumya Bhakat, Rishika National Institute of Technology Department of Electrical Engineering Jamshedpur India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Brainware University Department of Computational Sciences West Bengal India Cambridge Institute of Technology Department of Management Ranchi India

ISBN: (纸本)9798350390728

This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. The ever-increasing need for power encourages the growth of Distributed Generation (DG). Islanding is one of the key challenges associated with excessive penetration of DG sources and can cause harm to the clients and their equipment. The islanding needs to be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In the suggestive technique, spectrogram images are acquired through the time-series signals that are derived from the point of common coupling (PCC). The features are extracted from these images using the histogram of oriented gradient (HOG). The resulting feature vector is then utilised as an input for both the training and testing processes of the ensemble classifier. For deriving the spectrogram image, the rate of change of voltage is utilised as a parameter. In order to evaluate the performance of the ensemble classifier, 5, 10, 20, and 25-fold cross-validations are performed. The findings from the classification analysis indicate that the utilisation of an ensemble classifier in conjunction with the histogram of oriented gradient feature for image classification yielded remarkable outcomes, including an accuracy of 97.45% and a validation error of 0.188. © 2024 IEEE.

关键词： Graphic methods

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Multi-model Deep Learning Approach to Classifying Lung and Colon Cancer of Histopathology Images 3rd

Multi-model Deep Learning Approach to Classifying Lung and C...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Singh, Onkar Sinha, Sweta Kanti Kundu, Subhra Kumar Singh, Koushlendra Machine Vision and Intelligence Lab Department of CSE National Institute of Technology Jamshedpur India Saar Diagnostics Oncopathology Jharkhand Ranchi India Medical Oncologist Apex Hospital Tata Jharkhand Jamshedpur India

ISBN: (纸本)9789819743582

Malignant lung tumors are the major cause of mortality and morbidity associated with cancer around the globe. Recently, there has been an increase in the frequency of lung cancer. Lung cancer must be diagnosed histopathologically in order for the patient to receive effective treatment. The present research focuses on the determination of histological images to demonstrate the application of the deep learning approach in the categorization of squamous-cell carcinoma and adenocarcinoma within the lung and colon. We proposed a multi-model architecture where the features of ResNet50V2 and VGG19 are merged by merging their properties. This multi-model is then applied using a number of fully connected layers, which are then modified for a specific classification objective. It is suitable for jobs where good classification performance demands the extraction and integration of many different kinds of complex visual data. To validate the suggested approach, the LC25000 dataset was employed. In the research we conducted on the LC25000 dataset, the proposed technique outperformed previously used convolutional neural network methods with regard to accuracy (99.00%), precision (99.3%), recall (99.3%), and FI_score (99.2%). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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An Optimized Method for Fertilizer Recommendations Based on Hybrid ANN-DE Approach 3

An Optimized Method for Fertilizer Recommendations Based on ...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Banu C, Shakeela Gayathiri Devi, G. Sundhari, M. Rukmani Devi, S. Patil, Harshal Jeyaganthan, C. Bangalore India R.M.D Engineering College Department of Science and Humanities Chennai India Grt Institute of Engineering and Technology Department of CSE Tiruttani India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computational Intelligence Chennai India Sri Balaji University Maharashtra Pune India Amet Business School Amet University Chennai India

ISBN: (纸本)9798350381931

Despite agriculture's importance to India's economy, the proposed approach still haven't figured out how to profitably utilize huge agricultural land. The fundamental reason is a lack of knowledge about soil. Different types of soil have their own peculiarities. Soil property testing is so crucial. There may be a lot of tools for soil analysis out there, but not all of them are trustworthy, and farmers still have to deal with the trouble of getting dirt from labs. But getting all soil types tested in a timely manner at the lab is no easy feat. The current soil analysis tools also lack translations for regional languages. Consequently, the farmer requires a tool capable of performing soil analysis. Preprocessing the data, choosing features, and training the model should be the top priorities. Before using any machine learning algorithm on a dataset, preprocessing is necessary. It is common practice to collect raw data from multiple sources. The proposed approach use statistical correlation coefficient and information gain in feature selection. It is required to select features before training DE-ANN models. The suggested method performs noticeably better than the two front-runners, DE and ANN. Using the method resulted in a 97.65% increase in accuracy. © 2024 IEEE.

关键词： Soils

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A Spam Classification Method based on NB and SVM 3

A Spam Classification Method based on NB and SVM

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3rd international conference on machine Learning and Computer Application, ICMLCA 2022

作者： Li, Liyun Artificial Intelligence School of Engineering Science Shanghai200336 China

ISBN: (纸本)9781510664814

The dataset used in this project is derived from the SMS spam classification dataset in the UCI Dataset Repository, and it is necessary to understand what the dataset looks like before pre-processing the data. The first step is text clean-up, and the second step is text feature extraction. This paper investigates and compares the performance of classifiers combining different dimensionality reduction methods on spam datasets to provide a reference for related classification studies. The project then uses the scikit-learn machine learning library to train the classifier, dividing the dataset into 75% training sets and 25% test sets, and introducing classifiers such as NB, IR, SVM for training. After classifier training is complete, test the result of the model on the test set. Use trained classification models to predict the category of a message (regular mail or spam) The result shows that the best performer among the various classifiers is the SVM. © 2023 SPIE.

关键词： Support vector machines

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