1MACHINE LEARNING Summary of paper and Explanation regarding Machine Learning Techniques It can be stated as the domain of Artificial intelligence, which completely push forward by concept by providing complete access to data. Machine can easily learn themselves about the ways to tackle a given problem. By making use of complex mathematical and statistical tool, machine learning can easily perform in independent way (Xuet al. 2020). The whole idea regarding automation of complex task is all about generate of high interest in the domain of networking. Expectation of different activities are there in both design and operation for communication network can be easily offloaded to various machines. Machine learning in various domain of networking have already matched the expectation in some areas like intrusion detection, classification of radio and cognitive radios. There are list of algorithm which are common algorithm which are classified as machine learning. The approaches in machine learning can easily go far beyond the possibilities and reader can have a number of fundamental number of books on the given subject. Some of the commonly known machine learning are supervised learning, unsupervised learning, semi- supervisedlearning,reinforcementlearning,overfitting,underfittingandmodelselection. Supervised learning is generally used a range of application like speech recognition, detection of spam and recognition of object (Klaineet al. 2017). The mere goal is all about predicting the value of one or more kind of output variable which has a given value for the vector of input. There is a need for training data collection, which consists of N samples of input variables. In unsupervised learning, the algorithm completely identifies various kind of unusual patterns in data, taking account the wavelength, BER and modulation. Some of the common successful application of unsupervised learning method are genes clustering, analysis of social
2MACHINE LEARNING network and lastly market research (Fontanaet al. 2016). Considering the unsupervised learning, training datasets comprises of only a collection of input variables. Unsupervised learning can easily address various task where cluster analysis stand out to be very much common. Clustering can be stated as the process for different grouping kind of data so that intra- cluster similarity stands out to be bit low. Their similarity is expressed in terms of distance function, which depends on data type. In semi-supervised learning, the oldest form stand out to be self-training. This stand out to be an iterative way where the only first step is labeled with data points. It is achieved by making use of supervised learning algorithm. In this, each step come up with unlabeled for training decision functions where the given points are completely used with original label data (Anandakumar and Umamaheswari 2017). Reinforcement learning is merely used generally for addressing its application in various domains like robotics, finance and inventory management. Here the key goal is all about learning a policy and mapping in between two state of environment. RL paradigm help the agent to learn by analyzing available actions. Under overfitting, underfitting and model selection, a suitable discussion has been carried outusingmachinelearningalgorithmsalongwithpropersolution.Bothoverfittingand underfitting stand out to be two sides of same coin. Overfitting comes into picture when the model is very much complex and available for datasets. In this given scenario, the model will completely fit the training data in close way, which is inclusive of samples and outliners. The ultimate result is very much poor generation (Ezumaet al. 2019).It ultimately provides inaccurate prediction for given new data points. At the other side of spectrum, underfitting results due to model selection which are not that much complex. It is merely enough to capture all the essential features in the provide data.
3MACHINE LEARNING Advantages and Disadvantages of the application in research project The use of mathematical way which is completely collected from machine learning (ML) discipline have ultimately drawn the attention of most researchers. The benefit for using machine learning in the domain of optical networks is Increased Data availability:The optimal systems are merely associated with huge number of monitors (Meidanet al. 2017). It provides the ability to various kind of information on the whole details on the given system like traffic traces, signal quality indicator. In addition, it also focus on alarm in case of equipment failure and secondly user behavior. The drawback of using machine learning in this research project are IncreasedcomplexityofSystem:Adoptingsomeoftheadvancedtransmission techniques are mainly enabled by coherent technology. Introduction of flexible principle of networking like EON paradigm has been designed and operational for optimalnetworks (Fontanaet al. 2016). It stand out to be bit complex whichis as a result of turnable parameters which needs to be taken into account. ML methods are found to be helpful in capturing some of the complex non-linear system behavior with simple training.
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4MACHINE LEARNING References Anandakumar, H. and Umamaheswari, K., 2017. Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers.Cluster Computing,20(2), pp.1505-1515. Ezuma, M., Erden, F., Anjinappa, C.K., Ozdemir, O. and Guvenc, I., 2019, March. Micro-UAV detection and classification from RF fingerprints using machine learning techniques. In2019 IEEE Aerospace Conference(pp. 1-13). IEEE. Fontana, F.A., Mäntylä, M.V., Zanoni, M. and Marino, A., 2016. Comparing and experimenting machine learning techniques for code smell detection.Empirical Software Engineering,21(3), pp.1143-1191. Klaine, P.V., Imran, M.A., Onireti, O. and Souza, R.D., 2017. A survey of machine learning techniquesappliedtoself-organizingcellularnetworks.IEEECommunicationsSurveys& Tutorials,19(4), pp.2392-2431. Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N.O., Guarnizo, J.D. and Elovici,Y.,2017.DetectionofunauthorizedIoTdevicesusingmachinelearning techniques.arXiv preprint arXiv:1709.04647. Xu, S., Hirota, Y., Shiraiwa, M., Tornatore, M., Ferdousi, S., Awaji, Y., Wada, N. and Mukherjee, B., 2020. Emergency OPM Recreation and Telemetry for Disaster Recovery in Optical Networks.Journal of Lightwave Technology.