In this paper, we provide a detailed examination of energy consumption within specific sections of the Pecan Street Data Set. We employ two primary method-ologies: the Additive Factorial Approximate MAP (AFAMAP) algor...
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In the realm of machine learning, addressing class imbalance is a prevalent challenge, especially in datasets where certain classes are underrepresented. Synthetic Minority Over-sampling Technique (SMOTE) emerges as a...
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Sketch education is an essential component of arts education. In recent years, with the development of society, the demand for sketch courses has been steadily increasing. However, the existing teaching resources are ...
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Exponential growth of Internet and social media during the past few decades has helped the users to pass any information without even analyzing. Out of these many are fake information which has got no authenticity and...
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The term ''Web analytics'' pertains to the act of monitoring, analysing, and creating reports regarding the use of a website, such as its web pages, images, and videos. By utilising web analytics, busi...
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Identifying the type of network traffic has several advantages, such as detecting and preventing applications that violate an organization's security policy or improving Quality of Service (QoS) and Quality of Exp...
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ISBN:
(纸本)9798350333398
Identifying the type of network traffic has several advantages, such as detecting and preventing applications that violate an organization's security policy or improving Quality of Service (QoS) and Quality of Experience (QoE) through traffic engineering. To enhance QoS support for Internet Service Providers (ISPs), a fine-grained classification scheme for network traffic is proposed in this paper. Statistical analysis of the throughput patterns of FTP, video conferencing, and video streaming traffic reveal that using new statistical features can be more effective at distinguishing the Internet traffic, especially from a QoS perspective, compared to the features commonly used in the literature, even for encrypted traffic. In this work, machine learning algorithms for classifying the low-latency traffic are trained using combinations of statistical features including the novel trend identification. Experiments are conducted to evaluate the proposed method using large-scale real network traffic data. Results show that our method can classify the particular type of traffic with accuracy of over 97%, and identify the low-latency traffic in the traffic mix with accuracy of 87%.
Cloud computing contains lots of processing power and storage. Cloud computing and machine learning (ML) techniques enable large-scale data processing. The enhanced ML-based categorization technique is established in ...
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Federated learning (FL) has grown in popularity among universities and industrial businesses in processing and analyzing large amounts of data from IoT devices. Maintaining FL efficiency by preserving the privacy of I...
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The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learnin...
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Despite the valuable insights offered by both MRI and CT scans for brain diagnosis and treatment, limitations like cost, radiation exposure, and modality unavailability can restrict their combined use. This work addre...
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