DETERMINING LEARNING SUCCESS of kNN ALGORITHM on ZOO DATASET

People learn by examining, observing and researching their environment. They actually gains experience from what they have learned. By using the experience they have gained, they can adapt to the new situation they encounter and make decisions. People always make decisions by comparing their previous knowledge while describing objects and classifying them. Similarities and differences to previously learned objects are very effective in decision making. It has been shown in the studies that the experiential learning method can also be used on machines. Intelligent machines and devices that use machine learning methods in their structure are widely used in many areas. Machine learning can be performed using different algorithms. These algorithms use the attributes of the objects in the data set when making decisions. Similarities and differences in the attributes of objects are obtained by comparing them with previous experiences. As a result of the comparison, a decision is made and predictions are made about the classes of the objects. In this study, kNN machine learning algorithm, which is a supervised learning method, was used on the Zoo dataset. In this data set, there are attributes of common living things. By using these attributes, the classes of living things in the data set are determined. The “k” neighbor value and weight parameter selected in the kNN algorithm affect the learning success. In this study, the effect of two parameters used in the kNN algorithm on learning success is shown. According to the results obtained, the "k=1" neighbor value and the "Distance Weight" parameter were selected and the highest success result was obtained.

Unveiling the impact of weight heterogeneity on random walks in weighted extended tree-like fractals

In this paper, we put forward a class of weighted extended tree-like fractals and further use them as test bed to unveil the impact of weight heterogeneity on random walks. Specifically, a family of weighted extended tree-like fractals are first proposed, which are parameterized by a growth parameter [Formula: see text] and weight parameter [Formula: see text]. Then, we explore standard weight-dependent walk on the networks by deploying three traps at initial three nodes. To this end, we derive analytically the average trapping time (ATT) to measure the trapping efficiency and the obtained results show that depending on values of [Formula: see text], ATT may grow sub-linearly, linearly and super-linearly with the network size. Besides, it can also quantitatively impact the leading behavior and pre-factor of ATT simultaneously. Finally, more challenging mixed weight-dependent random walk that takes non-nearest-neighbor hopping is addressed. Analytical solutions of ATT derived under this new scenario imply that weight parameter [Formula: see text] still can qualitatively, quantitatively steer leading behavior and quantitatively affect pre-factor of ATT. As to the stochastic parameter [Formula: see text] controlling mixed random walk, it could only impact the pre-factor of ATT and only have negligible effect on the leading behavior of ATT. In summary, this work could further augment our understanding of random walks on networks.

Generalized Lindley-Quasi Xgamma distribution

We obtained a new generalization of Lindley-Quasi Xgamma distribution by adding weight parameter to it through weighting technique and have shown the flexibility of proposed model. Expression for reliability measures, order statistics, Bonferroni curves & indices, Renyi entropy along with some other important properties are derived. Maximum likelihood estimation method is put to use for estimation of unknown parameters of proposed model. Simulation study for checking the performance of maximum likelihood estimates and for model comparison is carried out. Proposed model and its related models are fitted to real life data sets and goodness of fit measure Kolmogorov statistic & p-value, loss of information criteria’s AIC, BIC, AICC & HQIC are computed through R software to check the applicability of proposed model in real life. The significance of weight parameter is also tested by using likelihood ratio test for both randomly generated data as well as real life data.

Global and Local Information Adjustment for Semantic Similarity Evaluation

Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.

Real-Time Evaluation of Structural Integrity of Tubulars in Directional Wellbores

The actual forces acting on the drill string in directional drilling is relatively complex than vertical drilling. In this work, the different forces acting on the drill string during directional drilling are analyzed using actual drilling data. The calculation of such forces can help driller to predict downhole complications that are caused due to drill string failures. The estimation of effective tension force at the top of the drill string requires both true tension forces and buckling stability forces acting on the drill string. True tension is a function of weight component of the drill string, the forces acting on BHA due to change in cross-sectional area and bottom pressure force acting on the drill bit and the drag forces acting on the string. The buckling stability force is defined as the difference between the internal and external force acting on the drill string. The effective tension is used to calculate the hookload and normal forces acting on the drill string. The calculation of the hookload at the deadline can help the driller to compare with actual hookload and take corrective action before the complication occurs. Further, that requires the relationship between the effective tension force at the top of the drill string and the hookload measured at the deadline. Such a relationship can be established by knowing the efficiency of the rig components such as sheave, block and tackle system, hydraulic lines and weight parameter for remaining components. Considering the unavailability of the efficiency of these components, the following model parameters are introduced: sheave efficiency, correction factor for efficiency of block and tackle system, hydraulic lines and weight parameter for the remaining components. All the three parameters are estimated by tuning the model with actual directional drilling data. In another aspect, the true tension is used to locate the position of neutral point by calculating the axial stress along the drill string. The proposed model is capable of predicting the hookload at the deadline, position of neutral point and normal forces acting along the drill string. The abnormal behavior of the normal forces along the drill string is used to locate the key-seating zones. Further, the model is validated with actual directional drilling data and successfully implemented in real-time monitoring platform and the model is found to be capable of predicting downhole complications such as drill string parting and improper hole cleaning. This study is expected to provide theoretical bases for understanding the stability regions of directional well.

Average trapping time on weighted directed Koch network

Numerous recent studies have focused on random walks on undirected binary scale-free networks. However, random walks with a given target node on weighted directed networks remain less understood. In this paper, we first introduce directed weighted Koch networks, in which any pair of nodes is linked by two edges with opposite directions, and weights of edges are controlled by a parameter θ . Then, to evaluate the transportation efficiency of random walk, we derive an exact solution for the average trapping time (ATT), which agrees well with the corresponding numerical solution. We show that leading behaviour of ATT is function of the weight parameter θ and that the ATT can grow sub-linearly, linearly and super-linearly with varying θ . Finally, we introduce a delay parameter p to modify the transition probability of random walk, and provide a closed-form solution for ATT, which still coincides with numerical solution. We show that in the closed-form solution, the delay parameter p can change the coefficient of ATT, but cannot change the leading behavior. We also show that desired ATT or trapping efficiency can be obtained by setting appropriate weight parameter and delay parameter simultaneously. Thereby, this work advance the understanding of random walks on directed weighted scale-free networks.