This paper introduces a guide aimed at aligning academic learning with industry standards in Agile methodology and Scrum framework usage. Targeting students, educators, and industry practitioners, it addresses the pre...
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Due to the negligence of driver or to some exterior factors may cause many people lost their lives in road accidents. So, there is a vital requirement to develop an efficient and effective accident detection system wh...
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In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essenti...
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In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.
The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run ...
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Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of th...
Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.
In this groundbreaking research endeavor, we present a novel approach to breast cancer assessment, leveraging the power of deep learning and transfer learning techniques. Our methodology involves the fine-tuning of a ...
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ISBN:
(纸本)9798350384277
In this groundbreaking research endeavor, we present a novel approach to breast cancer assessment, leveraging the power of deep learning and transfer learning techniques. Our methodology involves the fine-tuning of a pre-trained DenseNet201 model using the extensive BreakHis dataset, aiming to achieve precise categorization of breast cancer tumors. The primary objective of our study is to enhance the accuracy and reliability of breast cancer diagnosis through the utilization of state-of-the-art deep learning architectures. Employing transfer learning, we fine-tuned the pre-trained DenseNet201 model on the BreakHis dataset, a comprehensive and diverse collection of breast histopathological images. This dataset encompasses various benign and malignant breast tumor cases, providing a robust foundation for our model to learn intricate patterns and features. During the training phase, our model exhibited remarkable performance, achieving an impressive accuracy of 97.00%. The validation phase further reinforced the model's capabilities, yielding a validation accuracy of 92.00%. These compelling results underscore the efficacy of our approach in accurately categorizing breast tumors, thereby contributing to the advancement of breast cancer diagnostics. This research not only showcases the potential of deep learning in the field of medical image analysis but also emphasizes the importance of leveraging transfer learning to optimize model performance. The ability to discern subtle patterns in histopathological images enables our model to provide clinicians with reliable information for more accurate and timely breast cancer diagnosis. Our study signifies a significant step forward in the ongoing efforts to improve breast cancer assessment methodologies, with potential implications for enhancing patient outcomes through early and precise detection. The integration of advanced technologies, such as deep learning, into medical diagnostics holds promise for revolutionizing the w
Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised S...
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In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only address...
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In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.
autism spectrum disorder (ASD) is a neurological condition that disturbs an individual's capability to attach and communicate with others. It instigates in childhood and continues beyond adolescence and adulthood....
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Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classi...
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