Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...
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Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart ***,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack ***,solutions are derived by two different ***,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability ***,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy *** simulation results show the efficiency and superiority of our proposed framework.
In the early diagnosis of children with autism, the current main methods are based on doctor's clinical observation, eeg and ocular EEG methods, which require complex equipment and difficult acquisition process. B...
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Aspect-based sentiment classification is a fine-grained task aimed at inferring the sentiment polarities over specific aspect words in a sentence. Unlike previous approaches that mostly model directly on a given sente...
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Electroencephalogram (EEG), a non-invasive method of brain signal acquisition, is an important part of the research of motor-imagery brain-computer interface (MI-BCI). However, the collected EEG dataset are often cont...
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Within artificial intelligence, deep learning is a subset that emulates the information processing mechanism of the human brain. allowing machines to learn and make decisions on their own. This technology has revoluti...
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Membrane computing is a new computing paradigm with great significance in the field of computerscience. The Multi-membrane search algorithm (MSA) is proposed based on the membrane computational population optimizatio...
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In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are...
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Biometrics has received extensive attention due to its accuracy and convenience. However, commonly used biometric features still have deficiencies. Facial recognition is a potential threat to privacy, and iris recogni...
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Nowadays, in the social media's influenced digital era, managing the brand reputations especially automobile industry is very challenging due to the fast evolving mechatronics' technologies and incorporations ...
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Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;i...
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Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis *** deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical *** Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare *** recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image *** medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial *** newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death *** on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this *** context,primarily image quality enhancement procedures like noise removal and contrast normalization are *** the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are *** extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant *** selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar *** proposed model is evaluated in terms of accuracy,precision,recall,and f-
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