Stock Trend Prediction using Deep Neural Networks in Time Series and Social Sentiment Analysis

Ravichandran, P.; Rose, J Innila; Vijayakumar, K.

doi:10.4108/eai.16-5-2020.2304210

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The oddity location models are sorted into three in particular measurable, AI and a profound learning model [2,[4][5][6][7]. The factual model gathers and analyzes every information record and fabricates a measurable model.…”

Section: Literature Reviewmentioning

confidence: 99%

Network Anomaly Detection Using Hybrid Deep Learning Technique

Suganthi¹,

Nagarajan

Muhtumari³

2022

Advances in Parallel Computing

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Deep learning based intrusion detection system has acquired prominence in digital protection frameworks. The fundamental component of such a framework is to give an assurance to the ICT foundation in the interruption recognition framework (IDS). Wise arrangements are exceptionally essential and expected to control the intricacy and identification of new assault types. The smart frameworks, for example, Deep learning and Machine learning have broadly been acquainted with its advantages with actually manage intricate and layered information.The IDS has various types of known and unknown attacks, however there is a chance to improve the detection of attacks on implementing in real case scenario. Thus, this paper proposes a hybrid deep learning technique that combines convolutional neural network model with Long short term memory model to improvise the performance in recognizing the anomaly packets in the network. Experimentation has been carried out with NSL KDD dataset and the performances are compared with the traditional machine and deep learning models in terms of common metrics such as accuracy, sensitivity and specificity.

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Section: Literature Reviewmentioning

confidence: 99%

Network Anomaly Detection Using Hybrid Deep Learning Technique

Suganthi¹,

Nagarajan

Muhtumari³

2022

Advances in Parallel Computing

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“…Zia Ullah et al (2017 ) , used Hybrid technique ARIMA model with neural Network approach to predict resource utilization in Infrastructure as a Cloud. Ravichandran et al (2020), present the prediction of time series data utilizing the resources using deep neural networks. Qiu et al (2016), used Restricted Boltzmann Machines (RBM) with a regression layer DBN to estimate the CPU utilization in VMs.…”

Section: Review Of Literaturementioning

confidence: 99%

Resource utilization prediction technique in cloud using knowledge based ensemble random forest with LSTM model

Valarmathi¹,

Raja²

2021

Concurrent Engineering

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Future computation of cloud datacenter resource usage is a provoking task due to dynamic and Business Critic workloads. Accurate prediction of cloud resource utilization through historical observation facilitates, effectively aligning the task with resources, estimating the capacity of a cloud server, applying intensive auto-scaling and controlling resource usage. As imprecise prediction of resources leads to either low or high provisioning of resources in the cloud. This paper focuses on solving this problem in a more proactive way. Most of the existing prediction models are based on a mono pattern of workload which is not suitable for handling peculiar workloads. The researchers address this problem by making use of a contemporary model to dynamically analyze the CPU utilization, so as to precisely estimate data center CPU utilization. The proposed design makes use of an Ensemble Random Forest-Long Short Term Memory based deep architectural models for resource estimation. This design preprocesses and trains data based on historical observation. The approach is analyzed by using a real cloud data set. The empirical interpretation depicts that the proposed design outperforms the previous approaches as it bears 30%–60% enhanced accuracy in resource utilization.

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