Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content i...
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Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cl...
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Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
Predicting energy consumption has become crucial to creating a sustainable and intelligent environment. With the aid of forecasts of future demand, the distribution and production of energy can be optimized to meet th...
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Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with...
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The judicial process in India is often hindered by inefficiencies, underreporting of crimes, and manipulation of crime scenes by certain law enforcement officers. This paper presents a comprehensive solution through a...
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The growth in Internet audio data has highlighted the need for accurate and efficient search methodologies. In this context, query-by-example spoken term detection (QbE-STD) plays a pivotal role, mainly when dealing w...
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Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are ...
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This research focuses on abstractive text summarization techniques for regional languages, specifically Hindi. It employs a Transformer-based model to generate rephrased summaries from datasets of local language news ...
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The efficient scheduling of tasks on virtual machines (VMs) is paramount in cloud computing environments. The complexity and dynamism of today's applications require a more insightful and adaptive approach to task...
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ISBN:
(纸本)9798331300579
The efficient scheduling of tasks on virtual machines (VMs) is paramount in cloud computing environments. The complexity and dynamism of today's applications require a more insightful and adaptive approach to task allocation to ensure optimal resource utilization and service delivery. Traditional scheduling approaches often fall short when it comes to considering the multi-dimensional attributes of tasks and VMs, such as makespan, deadline, memory, and bandwidth requirements. These methodologies lack the ability to dynamically adapt to the ever-evolving requirements of tasks and the capacities of VMs, leading to suboptimal performance and resource wastage. In this paper, we present a novel approach that fuses BiLSTM & BiGRU with Exponential Smoothing Recurrent Neural Network (ES-RNN) to create a more robust and adaptive task scheduling mechanism under real-time scenarios. This model holistically assesses task capacity based on its makespan, deadline, memory, and bandwidth requirements. Similarly, VM capacity is evaluated based on its RAM, MIPS, bandwidth, and the number of processing elements. The fusion of these advanced neural architectures provides a deeper understanding of the task-VM mapping, enabling a more intelligent and efficient scheduling decision. Our approach demonstrates a marked improvement over traditional techniques, with tangible benefits such as reduced makespan by 4.9% and improved VM computation efficiency by 3.5%. The practical implications of our methodology are profound. By integrating our model into real-world cloud environments, organizations can expect to see an enhanced deadline hit ratio by 1.5%, ensuring that critical tasks meet their time-sensitive objectives. Moreover, the decision-making process becomes significantly more agile, resulting in a decision delay reduction of 4.5%, thereby promoting more responsive and efficient cloud computing operations. This work paves the way for a new era of intelligent cloud resource management, opt
With the growing trend of cloud computing, the necessity for secure data storage arises as traditional security measures fail with different types of new emerging cyber threats. The paper introduces a file storage sys...
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