Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

2020 ◽  
pp. 59-84
Author(s):  
Ivo Bukovsky ◽  
Peter M. Benes ◽  
Martin Vesely
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Identification ◽  
Control Loops ◽  
Efficient Machine ◽  
Incremental Gradient Descent ◽  
And Control ◽  
Model Reference Adaptive ◽  
Neuro Controller

This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

scholarly journals Proposed approach to detect distributed denial of service attacks in software defined network using machine learning algorithms

2018 ◽  
Vol 7 (2.8) ◽  
pp. 472 ◽  
Author(s):  
Shruti Banerjee ◽  
Partha Sarathi Chakraborty ◽  
. .
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Paper Machine ◽  
Software Defined Network ◽  
Distributed Denial Of Service ◽  
Control Plane ◽  
Data Plane ◽  
And Control

SDN (Software Defined Network) is rapidly gaining importance of ‘programmable network’ infrastructure. The SDN architecture separates the Data plane (forwarding devices) and Control plane (controller of the SDN). This makes it easy to deploy new versions to the infrastructure and provides straightforward network virtualization. Distributed Denial-of-Service attack is a major cyber security threat to the SDN. It is equally vulnerable to both data plane and control plane. In this paper, machine learning algorithms such as Naïve Bayesian, KNN, K Means, K-Medoids, Linear Regression, use to classify the incoming traffic as usual or unusual. Above mentioned algorithms are measured using the two metrics: accuracy and detection rate. The best fit algorithm is applied to implement the signature IDS which forms the module 1 of the proposed IDS. Second Module uses open connections to state the exact node which is an attacker and to block that particular IP address by placing it in Access Control List (ACL), thus increasing the processing speed of SDN as a whole. 

MARTT: Automatic Markup of Taxonomic Descriptions with XML

2013 ◽  
Author(s):  
Hong Cui
Keyword(s):  
Machine Learning ◽  
Information Content ◽  
Large Scale ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Taxonomic Descriptions ◽  
Efficient Machine

Despite the sub-language nature of taxonomic descriptions of animals and plants, researchers have warned about the existence of large variations among different description collections in terms of information content and its representation. These variations impose a serious threat to the development of automatic tools to structure large volumes of text-based descriptions. This paper presents a general approach to mark up different collections of taxonomic descriptions with XML, using two large-scale floras as examples. The markup system, MARTT, is based on machine learning methods and enhanced by machine learned domain rules and conventions. Experiments show that our simple and efficient machine learning algorithms outperform significantly general purpose algorithms and that rules learned from one flora can be used when marking up a second flora and help to improve the markup performance, especially for elements that have sparse training examples.Malgré la nature de sous-langage des descriptions taxinomiques des animaux et des plantes, les chercheurs reconnaissent l’existence de vastes variations parmi différentes collections de descriptions, en termes de contenu informationnel et de leur représentation. Ces variations présentent une menace sérieuse pour le développement d’outils automatiques pour la structuration de larges… 

scholarly journals USE OF MACHINE LEARNING ALGORITHMS IN CYBER SECURITY

2021 ◽  
Vol 9 (2) ◽  
pp. 1214-1219
Author(s):  
Sheha kothari, Et. al.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
New Technology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
And Control

Artificial intelligence (AI) has made incredible progress, resulting in the most sophisticated software and standalone software. Meanwhile, the cyber domain has become a battleground for access, influence, security and control. This paper will discuss key AI technologies including machine learning in an effort to help understand their role in cyber security and the implications of this new technology. This paper discusses and highlights the different uses of machine learning in cyber security.

scholarly journals Machine Learning Algorithms are Pre-Programmed to Humans

2019 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Infinite Number ◽  
Learning Algorithms ◽  
Predatory Behavior ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Entire Population ◽  
Intellectual Level ◽  
And Control

In the next 25 years, AI will evolve to the point where it will know more on an intellectual level than any human. In the next 50 or 100 years, an AI might know more than the entire population of the planet put together. At that point, there are serious questions to ask about whether this AI - which could design and program additional AI programs all on its own, read data from an almost infinite number of data sources, and control almost every connected device on the planet - will somehow rise in status to become more like a god, something that can write its own bible and draw humans to worship it. The problem is that The Machine Learning Algorithms are Pre-Programmed to Humans and may lead to Predatory Behavior like Bio-Robots [1].

AUTOMATIC ESTIMATION OF SPEECH MESSAGE QUALITY IN MOBILE COMMUNICATION SYSTEMS USING MACHINE LEARNING ALGORITHMS BASED ON THE CONSTRUCTION OF DECISION TREES

2020 ◽  
Author(s):  
А.Н. ВИНОГРАДОВ ◽  
А.С. СУРМАЧЕВ
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Mobile Communications ◽  
Communication Systems ◽  
A Priori ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Signal Features ◽  
A Priori Uncertainty ◽  
And Control

Предлагается метод выявления характерных искажений речевого сигнала в системах подвижной радиосвязи в условиях априорной неопределенности относительно условий приема сигнала и его качества. Предлагаемый метод базируется на использовании алгоритмов машинного обучения, в частности, аппарата построения деревьев решений и их множеств. Приводится подробное описание используемых для классификации признаков сигналов, а также характеристики обучающей и контрольной выборок. Приведены фрагменты кода программ, отражающие основные ключевые моменты их работы, и экспериментально полученные результаты. It is proposed a method of detecting specific distortions in mobile communications systems under conditions of a priori uncertainty of signal reception conditions and its quality. The proposed method is based on the use of machine learning algorithms, in particular construction of decision trees and their ensembles. A detailed description of signal features used for classification, as well as characteristics of training and control samples, are provided. Program code fragments that implement basic working stages and experimentally obtained results are given.

scholarly journals The algorithmic fix: Location intelligence, placemaking, and predictable futures

2021 ◽  
pp. 135485652110056
Author(s):  
Didem Özkul
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Policing ◽  
Location Determination ◽  
Data Practices ◽  
Key Aspects ◽  
And Control

With this article, I introduce the ‘algorithmic fix’ as a framework to analyze contemporary placemaking practices. I discuss how algorithmic practices of placemaking govern and control mobilities. I theorize such practices as the ‘algorithmic fix’, where location determination technologies, data practices, and machine learning algorithms are used together to ‘get a fix on’ our whereabouts with the aim of sorting and classifying both people and places. Through a case study of location intelligence, I demonstrate how these digital placemaking practices do not only control and prevent physical mobilities – they are designed to fix who we are and whom we may become with the aim of creating a predictable future. I focus on geo-profiling, geo-fencing, and predictive policing as three key aspects of location intelligence to present a discussion of how ‘algorithmic fix’ as a framework can provide valuable insights to analyzing contemporary placemaking practices.

scholarly journals Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1432
Author(s):  
Nimra Munir ◽  
Michael Nugent ◽  
Darren Whitaker ◽  
Marion McAfee
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Process Analytical Technology ◽  
Learning Algorithms ◽  
Hot Melt Extrusion ◽  
Machine Learning Algorithms ◽  
Melt Extrusion ◽  
Future Directions ◽  
And Control ◽  
Hot Melt

In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.

scholarly journals The Disease prediction system using Machine learning

2020 ◽  
Vol 9 (2) ◽  
pp. 24948-24952
Author(s):  
Deepika Joshi ◽  
Renu Kant ◽  
Sachin Shakya
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Medical Field ◽  
Huge Amount ◽  
Medical Decisions ◽  
Accurate Analysis ◽  
Diabetes Status ◽  
Efficient Machine ◽  
Applications Of Machine Learning

As, rise in the field of technology machine learning is widely used in various fields. Now it has various applications on the field of health industry. It works as a helping hand for the field of health industry. By the help of various machine learning algorithms, we can make various models for predicting the results through the large amount of dataset present in medical field. This paper comprises of efficient machine learning algorithms used in predicting disease through symptoms. As, the health industry has a huge amount of data for various fields so, we want to make a system where we can use various other applications of machine learning on health industry. This all had been done to make the better medical decisions and also for rise in the accuracy. As accurate analysis of the early prediction of disease helps in the patient care and the society services. These all challenges can be easier by the help of various tools, algorithms and framework provided by the machine learning. In addition to all these predictions we are making a chatbot for all that where patients can add the symptoms that are helpful to predict the disease and also check their diabetes status through the various information provided to system by the patients.      

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