*<IP_ADDRESS> K-Nearest Neighbour (K-NN)*

K-Nearest Neighbour is one of the most effective machines gaining knowledge of algorithms based totally on Supervised getting to know method. The K-NN algorithm makes the assumption that the new case and the existing cases are comparable, and it places the new instance in the category that is most like the existing categories. A new data point is classified using the K-NN algorithm based on similarity after all the existing data has been stored. This means that utilising the K-NN method, fresh data can be quickly and accurately sorted into a suitable category. Although the K-NN approach is most frequently employed for classification problems, it can also be

*Machine Learning Algorithms from Wireless Sensor Network's Perspective DOI: http://dx.doi.org/10.5772/intechopen.111417*

utilised for regression. Since K-NN is a non-parametric technique, it makes no assumptions about the underlying data [18].

It is also known as a lazy learner algorithm since it saves the training dataset rather than learning from it immediately. Instead, it uses the dataset to perform an action when classifying data. The KNN method simply saves the information during the training phase, and when it receives new data, it categorises it into a category that is quite similar to the new data.

The K-NN algorithm is excellent for WSN query processing jobs because of its simplicity.

The following algorithm can be used to describe how the K-NN works:

**Step 1:** Decide on the neighbours K-numbers;

**Step 2**: Calculate the Euclidean distance (or Hamming Distance) between K neighbours. The distance between two points, which we have already examined in geometry, is known as the Euclidean distance;

E.g.: Let there be two points A(x1,y1) and B(x2,y2). Now the Euclidean distance between them can be calculated as: ED ¼ √ ð Þ x2 � x1 2 þ y2 � y1 � �2 h i.

**Step 3:** Based on the determined Euclidean distance, select the K closest neighbours;

**Step 4:** Count the number of data points in each category among these k neighbours;

**Step 5:** Assign the fresh data points to the category where the neighbour count is highest;

**Step 6:** Model is complete.

#### **Some pros of using the KNN algorithm:**

1. It is straightforward to put in force;

2. It's far strong to the noisy schooling records;

3. It can be extra powerful if the training facts are huge.

#### **Some cons of using the KNN algorithm:**

1.K's value must always be determined, and sometimes that can be difficult;

2.The high computation cost is caused by the need to determine the separation between each data point for each training sample.

#### **2.2 Unsupervised learning**

In supervised machine learning, models are trained on labelled data while being watched over by training data. However, there may be several instances when lacking labelled data and need to identify hidden patterns in the supplied dataset. Therefore, one needs unsupervised learning strategies to handle these kinds of problems in machine learning. Unsupervised learning is a subcategory of machine learning wherein models are trained using unlabelled datasets and are free to operate on the data without being checked by a human observer.

Because unlike supervised learning, one has the input data but no corresponding output data, unsupervised learning cannot be used to solve a regression or classification problem directly. Finding the underlying structure of a dataset, classifying the data based on similarities, and representing the dataset in an unsupervised way and in the compressed format are the objectives of unsupervised learning [19].

The following are a few key arguments for the significance of unsupervised learning:

According to the **Figure 13**, input data is unlabelled, which means that neither its category nor any associated outputs are provided. Now, the machine learning model is being trained using the unlabelled input data. It will first evaluate the raw data to identify any hidden patterns in the data before applying the appropriate algorithms. The unsupervised leaning algorithm has two subtypes: Clustering and Association.


**1. Introduction**

500 Numerical Simulation – From Theory to Industry

*version 2.1*, MathWorks Inc.

Vladimirescu, A. (1999). *SPICE*, Ed.Tehnica, Bucuresti

Vladimirescu, A. (1997) SIMULINK ; *Dynamic System Simulation for MATLAB, User's Guide,*

The purpose of this book chapter is to analyze from a numerical point of view a reaction diffusion mathematical modelling of titanium carbide combustion synthesis from a mixture of titanium and carbide reactive powders thanks to self-propagating high temperature synthesis process. This modelling results in the coupling between a nonlinear parabolic equation expressing the enthalpy balance of the system with radiative boundary conditions and a nonlinear differential equation describing the exothermic chemical reaction in the system. An another multiphysics coupling was analyzed in [3]. This Self-propagating High temperature Synthesis (SHS) process was discovered in 1965 by Merzhanov [7], [8] and uses the energy released by the exothermic reaction itself to ensure its self-propagation inside the material after a localized heat supply has been performed for several seconds on the surface of the solid mixture. The stoichiometric solid mixture is made of several kinds of reactive powders. We analyze in this book chapter, the influence of radiative boundary conditions *related to the heat supply* over the ignition and eventual propagation of a combustion front inside the material.

Four sections are used to present our numerical simulation work. Section two presents the governing equations of the modelling. Section three outline the main aspects of the numerical scheme. Section four analyzes and discusses the main numerical simulation results of the combustion synthesis process. A conclusion summarizes the results that were obtained.


**7. Intramedullary stems**

In revision total knee arthroplasty, the mechanical stability of the femoral and tibial components is increased by the addition of intramedullary stems. Whatever solution is indicating to compensate bone loss, the use of an extended femoral and tibial stems reduce the stress forces on the metaphyseal region and bone-implant interface. Stem extensions, with or without offset, can supplement fixation, decrease stress at the bone-implant interface, and help address asymmetric bone defects. Offset stems can assist with implant alignment on the metaphysis, reducing the incidence of coronal or sagittal components malalignment and helping balance the flexion and extension spaces by effectively translating the components.

The stem ability to protect the proximal tibia or distal femur has been demonstrated in a laboratory setting using finite element model and cadaver models. Brooks et al used stems in conjunction with various bone augmentation techniques for defect in the proximal tibia: they suggested that a 70-mm stem carried from 23% to 38% of the axial load (Brooks et al., 1984).

A finite element analysis has revealed that the predicted bone loss is even greater in stemmed components compared to stemless ones; this may have consequences to discouraged routine use of stems in revision TKA (van Lenthe et al., 2002). Nevertheless Stern and Insall (Stern & Insall, 1992) advocates routine use of stemmed components in revision TKA. Engh et al used femoral stems mainly to protect large structural grafts in revision TKA (Engh et al., 1997). Meneghini et al. advise the use of cemented stemmed extension to maximize early implant xation and allow for successful biologic ingrowth of the TM cones into the remaining part of the bone (Meneghini et al., 2008). We recommend to add stem extensions when using metal augments to decrease stress at the bone-implant interface.

Cemented stems allow for intraoperative adjustment with unusual anatomy and achieve fixation in large canals and osteopenic bone (Murray et al., 1994). The main disadvantages are that they are difficult to remove if revision is necessary and since they are not canal filling, they do not guarantee alignment (Parsley et al., 2003). Cementless press-fit stem extensions are easy to use and facilitate component alignment, and diaphyseal engaging stems ensure fixation (Radnay & Scuderi, 2006). In our practice with stemmed components of TKA, we prefer to use hybrid fixation in both femur and tibia, with proximal cementation just in the metaphyseal area as usual. In fact, Jazrawi et al. demonstrated in a cadaveric study that a press-fit modular cementless stem could achieve equivalent stability than a somewhat shorter fully cemented stem (Jazrawi et al., 2001), whereas Albrektsson et al. showed with radiostereogrammetic analysis that a long cementless stem provide optional stability (Albrektsson et al., 1990).


**9. Drug treatment that targets NMDA receptors**

Typically, drug treatment for schizophrenia patients consists of antipsychotics, such as clo‐ zapine, that target D2 receptors to relieve the positive symptoms. However, a significant portion of schizophrenia subjects do not respond well to D2 antagonists; moreover, the nega‐ tive and cognitive impairments are barely affected by treatment with antipsychotic drugs. As a result, medical professionals are testing new pharmacological agents that target NMDA receptors as a therapeutic option. Due to the observed glutamate hypofunction impairment in patients, investigative studies have focused on the enhancement of NMDA receptor function. Therefore, high doses of glycine agonists (60 g/day) that act upon the glycineB modulatory site are used to increase NMDA receptor function [13]. These agonists have been shown to mod‐ estly improve the negative and positive symptoms of schizophrenia and are currently being utilized as an adjunctive treatment to primary therapy with D2 antagonists. An alternative option is to target the glycine transporter‐1 (GlyT‐1) with the selective inhibitor, sarcosine, to increase glycine availability for NMDA receptor binding [14]. Sarcosine administered at 2 g/day have shown to improve negative and cognitive symptoms of schizophrenia. Other drugs include *d*‐Serine and *d‐*amino acid oxidase (DAAO) inhibitors to increase *d*‐Serine availability, as it is considered a co‐agonist to NR1. The effects of the inhibitor were shown to alleviate negative and cognitive impairments in patients when administered at high doses (>2 g/day) and as a supplement to antipsychotic treatments [14]. Kynurenine aminotransfer‐ ase II (KATII) inhibitors are used to block kynurenic acid (an endogenous antagonist at the NR1 glycineB site) used to improve negative symptoms. NR2 subtype selective modulators are still under drug development and could prove beneficial to a neurological disorder, such as schizophrenia. Other pharmacological drugs available that affect glutamate transmission are utilized to target AMPA receptors, mGlu5 receptors, and NMDA receptors on GABAergic interneurons. However, many of these new therapeutic interventions are still in clinical trials, whether they are more effective than the typical and atypical D2‐related antipsychotic drugs remains to be determined.