Reduced-order modeling of high-speed channels using machine learning techniques: Partitional and hierarchical clusterings

The importance and growth of the digital IC have become more popular because of parameters such as small feature size, high speed, low cost, less power consumption, and temperature. There have been various techniques and methodologies developed so far using different optimization algorithms and data structures based on the dimensions of the IC to improve these parameters. All these existing algorithms illustrate explicit advantages in optimizing the chip area, maximum temperature of the chip, and wire length. Though there are some advantages in these traditional algorithms, there are few demerits such as execution time, integration, and computational complexity due to the necessity of handling large number of data. Machine learning techniques produce vibrant results in such fields where it is required to handle big data in order to optimize the scaling parameters of IC design. The objective of this chapter is to give an elaborate idea of applying machine learning techniques using Bayesian theorem to create automation tool for VLSI 3D IC design steps.

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Review on Anomaly Detection in Mobile Networks Using Traditional Learning, Machine Learning and Deep Learning

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9054 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4789-4796

Author(s):

T. S. Prabhakar ◽

M. N. Veena

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Mobile Networks ◽

High Speed ◽

Network Activity ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Artificial Neural Network Method ◽

Learning Machine

Increasing usage of smart phones involves in the developing large amount of data and high speed internet is used for transfers this large amount of data. This in-turn gives rise to the development of various attacks to hack the data. Anomaly detection in the network analyzes the pattern in the network activity and found the abnormality in the network. The accurate detection of abnormality in network helps to prevent the attackers to steal the data. Many researches were conducted to improve the performance of anomaly detection in the mobile networks. Traditional methods results for performance of anomaly detection are not much effective. Machine learning techniques are used for the anomaly detection to increase the performance. The deep learning techniques are applied to increase the detection rate and decrease the false positive. Both the techniques machine learning uses k-means and Deep learning uses Artificial Neural Network method provides the considerable performance in anomaly detection.

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Comparison of Machine Learning Techniques for Predictive Modeling of High-Speed Links

2019 IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) ◽

10.1109/epeps47316.2019.193199 ◽

2019 ◽

Cited By ~ 4

Author(s):

Hanzhi Ma ◽

Er-Ping Li ◽

Andreas C. Cangellaris ◽

Xu Chen

Keyword(s):

Machine Learning ◽

Predictive Modeling ◽

High Speed ◽

Machine Learning Techniques ◽

Learning Techniques

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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