This paper presents new viewpoints to solve predicative maintenance problems for arbitrarily rigid-body mechanical systems. Reliable predictions for changes in physical parameters are highly dependent on system model ...
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Given the variety of fire and smoke, which are distinguished by variances in texture and color, it is extremely difficult to detect fire and smoke from visual imagery. A significant amount of economic and environmenta...
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Data analytics have had a significant impact on tactical and workload planning in football. Football data is divided into two categories: event data, which captures on-the-ball events like passes and shots, and tracki...
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Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erro...
Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and *** automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chisquare(Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100%accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.
作者：
Grout, IanMullin, LenoreUniversity of Limerick
Faculty of Science and Engineering Department of Electronic and Computer Engineering Limerick Ireland University at Albany
SUNY College of Engineering and Applied Sciences Department of Computer Science AlbanyNY United States
In this paper, the Mathematic of Arrays (MoA) approach to solving complex multi-dimensional array computations using array indexing is considered with reference in its implementation in code targeting embedded systems...
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This research proposes an intelligent empathic robotic system for elderly healthcare, combining AI, Theory of Mind, and Affective Computing to deliver personalized care. The low-cost robot continuously monitors patie...
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The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the cit...
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The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efficient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.
Connected Components Labeling (CCL) is a technique to detect different objects in an image by assigning each object a unique label. CCL has been widely used in many applications. For example, in computer-Aided Diagnos...
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Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predict...
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Billions of physical devices around the world connected to the internet refer to the latest booming technology such as internet of things (IoT). Without human intervention, all collecting and sharing data around the g...
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