In today's world, there are many types of data. The text files, the images, the audio, and the videos are all data and have been integrated into everyone's life. But the storage requirement of data is huge as ...
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In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirement...
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In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semi-supervised learning task. By training the mode...
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The research presents an innovative browser extension that serves as a virtual sales assistant for shoppers across e-commerce platforms. Designed to foster trust and enhance the online shopping experience, this extens...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
The research presents an innovative browser extension that serves as a virtual sales assistant for shoppers across e-commerce platforms. Designed to foster trust and enhance the online shopping experience, this extension enables detailed product inquiries and provides immediate access to comprehensive specifications, empowering customers to make informed purchasing decisions. By seamlessly integrating with browsing sessions and leveraging natural language processing techniques, the virtual assistant generates accurate and informative responses to product-related queries, positioning e-commerce platforms as authoritative and responsive sources. Beyond functional capabilities, the extension aims to transform user engagement by acting as a personalized guide, akin to an in-store representative, thereby heightening satisfaction through tailored service and fostering deeper customer relationships. The research is motivated by factors such as increasing customer engagement, providing round-the-clock support, facilitating product differentiation and comparison, harnessing machine learning for enhanced experiences, building trust through expert guidance, ensuring unique integration with e-commerce platforms, iterating through feedback loops for continuous improvement, and supporting small businesses in enhancing customer interactions.
Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the ne...
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While hiding data in DNA sequence, the steganograpy schemes try to increase the embedding payload, enhance the security of implanted data, protect the stego expansion and preserve the biological characteristics of DNA...
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Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot a...
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Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. Th...
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Cellular ad Hoc Networks (MANETs) have become increasingly popular in cell computing packages, inclusive of cellular computing, fitness care, automobile, and army packages. As such, safety protocols used in the transm...
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The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement Learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing.
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