Healthcare facilities are becoming more and more integrated into our daily lives. Nurses are responsible for monitoring intravenous (IV) fluid levels in almost all hospitals. Most often, due to their busy schedules, p...
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COVID-19, often known as the coronavirus sickness of 2019, is a fast-spreading virus-related disease that has affected masses of people worldwide. Given its rapid spread and growing number, the healthcare personnel ar...
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This paper focuses on the application of adversarial autoencoders for improving the robustness of image generation. Therefore, in this study, adversarial training is proposed to be incorporated into the autoencoder st...
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Large segments of the population value sign language as one of the many languages used for communication. Each sign in each sign language differs in terms of hand shape, motion profile, and positioning of the hand. Th...
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This research introduces a novel visual cryptographic scheme using colour QR codes to enhance data security in defence applications. The method involves encrypting a secret image into shares that are embedded within a...
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Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper expl...
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Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of ma...
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Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent *** research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of ***,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential ***,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi *** Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI *** were carried out to evaluate the performance of the proposed Sole-SAM *** experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.
This paper focuses on the improvement of Quality of Service (QoS) in Wireless Sensor Networks by using Machine Learning (ML) techniques to optimize routing protocols. It addresses the essential QoS challenges such as ...
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In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basi...
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In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.
As the number of vehicles rises, there is a problem with traffic congestion on the roads. This problem is characterized by slower speeds, greater time spent travelling, and more congestion in the traffic lanes. In add...
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