Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence...
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Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on the physical coupled hidden Markov model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing, and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The aforementioned disadvantages lead to lower prediction accuracy of the CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then, the corresponding forward-backward algorithms are designed for resolving the evaluation problem of the CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into the CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further self-comparison experiments verify that our algorithms are effective and practical. Impact Statement-Through the analysis of users' behavior habits, consumption habits, preferences, etc., users? potential needs may be discovered. This discovery could help predict users' activities. If a waiter predicts the user's next activity. He gives her/him unexpected services to meet users' next needs. Obviously, it would significantly improve user satisfaction. In addition, connecting the front and rear products co
The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily fun...
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Cancer was found to be a leading cause of human mortality in the year 2020, accounting for one in six deaths worldwide, as per data published by the World Health Organization. Early detection and treatment can play a ...
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Cancer was found to be a leading cause of human mortality in the year 2020, accounting for one in six deaths worldwide, as per data published by the World Health Organization. Early detection and treatment can play a major role in averting these deaths. Delayed cancer care often leads to lower chances of survival, greater complications associated with treatment and higher costs. Histopathology image analysis is a technology that plays a vital role in the early detection and diagnosis of cancer. The segmentation of regions of interest (RoIs) from whole slide images (WSIs) provides useful information for differentiating diseased tissues from normal ones. A strong segmentation framework is required in this case due to the rich and irregular mix of visual patterns of the RoIs. In this work, we present an atrous inception-resnet based UNet model with dense skip connections (AIR-UNet++) for the effective segmentation and detection of various RoIs from histopathology images stained with Hematoxylin and Eosin (H &E). To test the performance of the proposed method, experiments are carried out on five different datasets, including nuclei segmentation, TNBC, MoNuSeg, lymphocyte detection and MoNuSAC (Lymphocyte, Neutrophils, Macrophages, Epithelial). Experimental results show that the proposed AIR-UNet++ method outperforms other UNet variants, pre-trained models. Specifically, for the nuclei segmentation dataset, we achieved a Dice coefficient (DC) of 0.74 and a Jaccard Index (JI) of 0.64. For the TNBC dataset, our method achieved a DC of 0.88 and a JI of 0.79, while on the MoNuSeg dataset, we obtained a DC of 0.79 and a JI of 0.67. For the Lymphocyte detection dataset, we achieved an accuracy of 0.98 and an F1 score of 0.88. Notably, in the MoNuSAC-Lymphocyte dataset, our model achieved a DC of 0.85 and a JI of 0.75. Similarly, for the MoNuSAC-Neutrophils dataset, the DC was 0.83 with a JI of 0.72, for MoNuSAC-Macrophages, the DC was 0.82 with a JI of 0.72, and for MoNuSAC-Ep
Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. ...
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Background: Virtualization adequately maintains increasing requirements for storage, networking, servers, and computing in exhaustive cloud data centers (CDC)s. Virtualization assists in gaining different objectives l...
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Background: Virtualization adequately maintains increasing requirements for storage, networking, servers, and computing in exhaustive cloud data centers (CDC)s. Virtualization assists in gaining different objectives like dedicated server sustenance, fault tolerance, comprehensive service availability, and load balancing, by virtual machine (VM) migration. The VM migration process continuously requires CPU cycles, communication bandwidth, memory, and processing power. Therefore, it detrimentally prevails over the performance of dynamic applications and cannot be completely neglected in the synchronous large-scale CDC, explicitly when service level agreement (SLA) and analytical trade goals are to be defined. Introduction: Live VM migration is intermittently adopted as it grants the operational service even when the migration is executed. Currently, power competence has been identified as the primary design requirement for the current CDC model. It grows from a single server to numerous data centres and clouds, which consume an extensive amount of electricity. Consequently, appropriate energy management techniques are especially important for CDCs. Methods: This review paper delineates the need for energy efficiency in the CDC, the systematic mapping of VM migration methods, and research pertinent to it. After that, an analysis of VM migration techniques, the category of VM migration, duplication, and context-based VM migration is presented along with its relative analysis. Results: The various VM migration techniques were compared on the basis of various performance measures. The techniques based on duplication and context-based VM migration methods provide an average reduction in migration time of up to 38.47%, data transfer rate of up to 51.4%, migration downtime of up to 36.33%, network traffic rate of up to 44% and reduced application efficiency overhead up to 14.27%. Conclusion: The study aids in analyzing threats and research challenges related to VM migration
Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more a...
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Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.
Effective task scheduling and resource allocation have become major problems as a result of the fast development of cloud computing as well as the rise of multi-cloud systems. To successfully handle these issues, we p...
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In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signal...
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In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.
Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
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Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge server...
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