With the progress of wireless communication technology and software technology, a large number of new intelligent applications and services are emerging, which puts higher demands on the latency and processing perform...
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The major goal of a secure coding is to enable the authentic receiver could successfully recover the original message from the source. However. the satellite communications provide an open wireless environment. It har...
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Many organizations have published yearly reports on cloud security threat intelligence. These reports have shown a drastic increase in security attacks on cloud computing services. Such reports end up gathering dust w...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981;9798350373974
Many organizations have published yearly reports on cloud security threat intelligence. These reports have shown a drastic increase in security attacks on cloud computing services. Such reports end up gathering dust without exploiting the reports' usefulness due to their sheer size. The threats have to be analysed timeously to provide and anticipate solutions before further security attacks occur. With the increase in cloud computing usage, there has been an increase in security breaches. The cloud also adds to an aspect of volatility in provisioning of services and at the edge components availability. Incidents which have already been captured in incidents reports can be used to conduct digital forensic investigations (DFIs). For DFI process to be conducted, there is need for the environment to be prepared beforehand, thus, to say, the environment such as the cloud, should be digital forensics ready (DFRy). Digital forensics readiness (DFR) assists by providing a proactive way for DFI process to be conducted. Artificial intelligence (AI) applications assist in the processing of security incidence reports. This paper proposed the use of un-supervised learning techniques in the field of AI by exploring security threat intelligence reports. AI algorithms are used in the automated analysis of large and complex datasets in intelligence reports thus greatly accelerating the prediction of security incidents in order to provide DFR to the computing environment and anticipated responses for the reduction of security incidents.
Multi-temporal collaborative analysis of port scenes can enhance the representation ability of image scenes, and image registration is required before multi-temporal analysis. In this paper, an image registration netw...
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In this work, the authors present Quantum Text Teleportation Protocol (QTTP) that uses Quantum Teleportation (QT) technique and Huffman coding for secure text transfers. The QTTP enables the teleportation of quantum s...
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This article presents a case study on the optimization of cloud expenses using machine learning and predictive resource scalability with Amazon Web Services (AWS). Estimate prospective resource requirements, analyze h...
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ISBN:
(纸本)9798350386813;9798350386820
This article presents a case study on the optimization of cloud expenses using machine learning and predictive resource scalability with Amazon Web Services (AWS). Estimate prospective resource requirements, analyze historical data, and automate scaling decisions. This study used various sources to investigate cloud management machine learning issues, ethics, and best practices. Containerized microservices, cloud application performance, and auto-scaling are discussed in the references. We anticipate that serverless architectures and peripheral computing will affect cloud cost optimization in the future. Amid the transformation of cloud management brought about by machine learning, explainable AI, autoML integration, and enhanced predictive analytics are emerging. This study underscores the revolutionary potential of machine learning for organizations traversing the dynamic cloud computing market and its crucial function in optimizing cloud expenditures after a comprehensive examination of essential aspects. Prevailing cost-related issues in the cloud include over-provisioning, complex pricing models, and lack of visibility/control over expenses. Advancing cloud resources, particularly through predictive resource scaling strate- gies, addresses these challenges by optimizing resource allocation based on real-time demand patterns, thus reducing unused capacity and optimizing costs.
The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizin...
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The rapid growth of Large Lan-guage Models, Text- to-Speech technologies, and Retrieval-Augmented Generation is bringing a new mode of accessing and understanding textual information. This work talks about how these t...
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5G wireless communication is a promising next-generation technology that offers fast, low-latency connections. However, due to the high path loss and penetration loss of 5G millimeter-wave (mmWave) signals, shadow are...
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With the advance of technology in this recent era, Internet of Things (IoT) devices has been increasing at a rapid pace. Edge computing, which utilizes physically close-to-edge computing resource, becomes popular to c...
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