The cloud computingtechnology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data *** utilizes on-demand resource provisioni...
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The cloud computingtechnology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data *** utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization *** this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud *** capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource *** is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into *** addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS *** further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM *** results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for *** statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
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
Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing work...
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Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation
Accurate and reliable wind power prediction is the key to realize the smooth grid connection of wind power generation. However, the influence of wind speed, wind direction, and air density and other factors leads to t...
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Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, ...
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The ability to predict diseases early is essential for improving healthcare quality and can assist patients in avoiding potentially dangerous health conditions before it is too late. Various machine learning technique...
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In many Wireless Sensor Networks (WSNs) applications, the relevant sensor node’s location information is essential in determining where the event or situation occurs. Therefore, localization is one of the critical ch...
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Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. Ho...
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The longest common substring (LCS) identification has many applications in Pattern matching, Automata Theory, Bioinformatics, especially in DNA arrangement examination. The LCS issue looks for the longest shared subst...
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As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and...
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As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.
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