Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difficult to screen all skin lesion patients due to costly *** are requiring a correct method ...
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Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difficult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.
Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT...
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Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising *** the other hand,in medical studies,a limited dataset decreases the a...
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Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising *** the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL *** this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with *** study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between *** comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of *** model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original *** data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,*** developed model using synthetic data obtained a higher accuracy value than the related studies in the ***,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.
The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional cachin...
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IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** ...
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IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.
Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding *** paper addresses these requirements t...
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Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding *** paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction *** study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network *** response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare *** establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/*** optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model *** assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel *** metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline *** study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.
software quality prediction is used at various stages of projects. There are several metrics that provide the quality measure with respect to different types of software. In this study, defect density is used as the f...
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Video surveillance is widely adopted across various sectors for purposes such as law enforcement, COVID-19 isolation monitoring, and analyzing crowds for potential threats like flash mobs or violence. The vast amount ...
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Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemina...
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Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.
The adoption of Extreme programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a...
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The adoption of Extreme programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a novel Scalable Agile Maturity Assessment Model (SAMAM) to address these barriers and facilitate the effective adoption of XP. The model is designed by leveraging established frameworks, including the Capability Maturity Model Integration (CMMI), software Outsourcing Vendor Readiness Model (SOVRM), and software Process Improvement Implementation Management Model (SPIIMM). Unlike traditional models that rely on predefined Key Process Areas (KPAs), SAMAM adopts 14 critical barriers (CBs) identified through a Systematic Literature Review (SLR) and corresponding practices as the foundation for its maturity levels. The study was conducted in four phases. First, an SLR was performed to identify 14 critical barriers to XP adoption and their respective mitigation practices. In the second phase, a survey questionnaire was administered within the software industry to validate the SLR findings and extract additional industry-relevant practices. The third phase involved the development of SAMAM, structured into five maturity levels using the identified barriers and practices instead of traditional KPAs. In the final phase, industrial case studies were conducted to evaluate the model’s effectiveness in real-world settings using the Motorola Assessment Tool. The findings demonstrate that SAMAM provides a comprehensive and scalable approach to assess and improve XP adoption maturity by systematically addressing critical adoption barriers. The model supports organizations in overcoming XP adoption challenges and achieving higher process maturity. The evaluation through case studies confirmed the practical applicability and effectiveness of the proposed model, contributing to the body of knowledge on agile methodologies and advancing XP adoption in the software developm
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