Video white balance is to correct the scene color of video frames to the color under the standard white illumination. Due to the camera movement, video white balance usually suffers temporal instability with unnatural...
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Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the...
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Medical Image Analysis (MIA) is integral to healthcare, demanding advanced computational techniques for precise diagnostics and treatment planning. The demand for accurate and interpretable models is imperative in the ever-evolving healthcare landscape. This paper explores the potential of Self-Supervised Learning (SSL), transfer learning and domain adaptation methods in MIA. The study comprehensively reviews SSL-based computational techniques in the context of medical imaging, highlighting their merits and limitations. In an empirical investigation, this study examines the lack of interpretable and explainable component selection in existing SSL approaches for MIA. Unlike prior studies that randomly select SSL components based on their performance on natural images, this paper focuses on identifying components based on the quality of learned representations through various clustering evaluation metrics. Various SSL techniques and backbone combinations were rigorously assessed on diverse medical image datasets. The results of this experiment provided insights into the performance and behavior of SSL methods, paving the way for an explainable and interpretable component selection mechanism for artificial intelligence models in medical imaging. The empirical study reveals the superior performance of BYOL (Bootstrap Your Own Latent) with resnet as the backbone, as indicated by various clustering evaluation metrics such as Silhouette Coefficient (0.6), Davies-Bouldin Index (0.67), and Calinski-Harabasz Index (36.9). The study also emphasizes the benefits of transferring weights from a model trained on a similar dataset instead of a dataset from a different domain. Results indicate that the proposed mechanism expedited convergence, achieving 98.66% training accuracy and 92.48% testing accuracy in 23 epochs, requiring almost half the number of epochs for similar results with ImageNet weights. This research contributes to advancing the understanding of SSL in MIA, providin
Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machi...
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Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC) and parameter transfer. The method is based on single-layer long short-term memory(LSTM), pre-trained inceptionv3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 *** features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.
The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE service...
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The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.
The Ethereum blockchain platform has witnessed a surge in crypto-cybercrimes, particularly phishing attacks, resulting in significant financial losses. Analyzing the Ethereum transaction network to detect phishing use...
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A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most o...
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MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(...
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MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on ***,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser *** a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper ***,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive *** heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.
The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern gener...
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Amid the global shift of smart manufacturing towards greener and more intelligent paradigms, the spatiotemporal coupling characteristics of dynamic heat conduction networks pose significant challenges for optimizing t...
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Internet of Things (IoT) technology quickly transformed traditional management and engagement techniques in several sectors. This work explores the trends and applications of the Internet of Things in industries, incl...
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