Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant inde...
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Every day,websites and personal archives create more and more *** size of these archives is *** comfort of use of these huge digital image gatherings donates to their ***,not all of these folders deliver relevant indexing *** the outcomes,it is dif-ficult to discover data that the user can be absorbed ***,in order to determine the significance of the data,it is important to identify the contents in an informative *** annotation can be one of the greatest problematic domains in multimedia research and computer ***,in this paper,Adap-tive Convolutional Deep Learning Model(ACDLM)is developed for automatic image ***,the databases are collected from the open-source system which consists of some labelled images(for training phase)and some unlabeled images{Corel 5 K,MSRC v2}.After that,the images are sent to the pre-processing step such as colour space quantization and texture color class *** pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation(JSEG).Thefinal step is an auto-matic annotation using ACDLM which is a combination of Convolutional Neural Network(CNN)and Honey Badger Algorithm(HBA).Based on the proposed classifier,the unlabeled images are *** proposed methodology is imple-mented in MATLAB and performance is evaluated by performance metrics such as accuracy,precision,recall and F1_*** the assistance of the pro-posed methodology,the unlabeled images are labelled.
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interfer...
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Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due ...
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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,...
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Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,m...
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With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.
This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initia...
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The cloud computing technology 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 computing technology 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.
The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***...
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The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***,attackers have used advanced techniques to evade defenses by transforming their malware into functionality-preserving *** systematically analyze such evasion attacks and conduct a large-scale empirical study in this paper to evaluate their impact on *** specifically,we focus on two forms of evasion attacks:obfuscation and adversarial *** the best of our knowledge,this paper is the first to investigate and contrast the two families of evasion attacks *** apply 10 obfuscation attacks and 9 adversarial attacks to 2870 malware *** obtained findings are as follows.(1)Commercial Off-The-Shelf(COTS)malware detectors are vulnerable to evasion attacks.(2)Adversarial attacks affect COTS malware detectors slightly more effectively than obfuscated malware examples.(3)Code similarity detection approaches can be affected by obfuscated examples and are barely affected by adversarial attacks.(4)These attacks can preserve the functionality of original malware examples.
For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, ...
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Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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