34 results for "Punit Gupta"
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Federated learning inspired Antlion based orchestration for Edge computing environment
Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse…
Also available at: DOAJ
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Hybrid Bacterial Colony Optimization and Particle Swarm Optimization for load balancing in fog computing
Fog computing minimizes latency and bandwidth consumption by processing data near the source, but it has the challenge of workload balancing across the dynamic and resource limited fog nodes. Uneven task assignment can result in bottlenecks, idle resources, and lowered Quality of Service (QoS)…
Also available at: DOAJ
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Fault aware task scheduling in cloud using min-min and DBSCAN
Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of…
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DBSCAN inspired task scheduling algorithm for cloud infrastructure
Cloud computing in today's computing environment plays a vital role, by providing efficient and scalable computation based on pay per use model. To make computing more reliable and efficient, it must be efficient, and high resources utilized. To improve resource utilization and efficiency in cloud…
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Mapping Activity-Based Segregation of Names in Dublin Using Google Point of Interest Data
The current generation of cities with vast cultures and heritage is influenced by various factors like immigrants from different countries, religious heritage, tourism, and many more factors. Segregation in geographical regions is one of the ways to find patterns in cities influenced by gender…
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Availability optimization of power generating units used in sewage treatment plants using metaheuristic techniques
Metaheuristic techniques have been utilized extensively to predict industrial systems’ optimum availability. This prediction phenomenon is known as the NP-hard problem. Though, most of the existing methods fail to attain the optimal solution due to several limitations like slow rate of convergence…
Also available at: DOAJ
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Neural network inspired efficient scalable task scheduling for cloud infrastructure
The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to…
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A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers
Virtualisation is a major technology in cloud computing for optimising the cloud data centre’s power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center’s size expands resulting in increased energy…
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Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack
In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make…
Also available at: DOAJ
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Augmented Ensemble Model (AEM) for health trends prediction on social networks
Social Media has given an exponential rise to an ever-connected world. Health data that was earlier viewed as hospital records or clinical records is now being shared as text over social media. Information and updates regarding the outbreak of a pandemic, clinical visit results, general health…
Also available at: DOAJ
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Stochastic modeling and parameter estimation of turbogenerator unit of a thermal power plant under classical and Bayesian inferential framework
The work reported in present study deals with the development of a novel stochastic model and estimation of parameters to assess reliability characteristics for a turbogenerator unit of thermal power plant under classical and Bayesian frameworks. Turbogenerator unit consists of five components…
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UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans
Gastrointestinal (GI) cancer is leading general tumour in the Gastrointestinal tract, which is fourth significant reason of tumour death in men and women. The common cure for GI cancer is radiation treatment, which contains directing a high-energy X-ray beam onto the tumor while avoiding healthy…
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An efficient ranking deep neural network algorithm for the prediction of Ca 2+ binding sites of the protein
The Ca2+ binding sites of proteins are critical for their function, particularly in processes such as signal transduction, enzyme regulation, and structural stability. In this study, the calcium-binding sites of NtEhCaBP1 (Entamoeba histolytica calcium-binding protein). This paper proposes…
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Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications
High-performance computing changes the way of computing. With emerging technologies like grid computing, cloud computing and smart health care application has changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to…
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Neural network inspired differential evolution based task scheduling for cloud infrastructure
In recent years, cloud computing has become an essential technology for businesses and individuals alike. Task scheduling is a critical aspect of cloud computing that affects the performance and efficiency of cloud infrastructure. During this pandemic where most of the healthcare services like…