The recent advancements and a flurry of deep learning architectures in the fields of computer vision and natural language processing have greatly benefited the task of creating natural language descriptions for images...
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This paper describes a liquid-mixture sensor based on a mushroom-shaped zeroth-order resonator (ZOR). The Jerusalem-shaped mushroom-like structure is designed to reduce the dimensions of a resonator and provide a high...
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The microservice based architecture is widely used in large software systems. Despite its profound advantages, introduction of microservices brings in many challenges, especially in terms of autoscaling. Layered Queue...
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Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerica...
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Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.
The Blockchain technology moves data nodes and data streams from one informatics center to another based on the importance of Bitcoins, resulting in a dedicated, public, and secure network for interpretation and devel...
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In this paper, the Hausdorff Fractal Fokas-Lenells (HFFL) equation with full nonlinearity is investigated. The travelling wave reduction technique is utilized to transform the HFFL equation into an ordinary differenti...
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In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic s...
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In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.
Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm ...
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Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.
Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementi...
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Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.
Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their att...
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Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.
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