In this article, we investigate the idea of ???? ?? -statistical boundedness of order ?? for sequences of fuzzy numbers. Additionally, we provide different inclusion relations between ???? ?? -statistical boundedness of order ?? and ???? ?? -statistical convergence of order ??.

Text mining, which derives information from written sources such as websites, books, e-mails, articles, and online news, processes and structures data using advanced approaches. The vast majority of SMS (Short Message Service) messages are unwanted short text documents. Effectively classifying these documents will aid in the detection of spam. The study attempted to identify the most effective techniques on SMS data at each stage of text mining. Four of the most well-known feature selection approaches were used, each of which is one of these parameters. As a result, the strategy that yielded the best results was chosen. In addition, another parameter that produces the best results with this approach, the classifier, was determined. The DFS feature selection approach produced the best results with the SVM classifier, according to the experimental results. In Average Results, DFS showed the best result of 93.5361 for accuracy criterion, while it reached the highest result of 93.4953 for Macro-F1. This study establishes a general framework for future research in this area that will employ text mining techniques.

In this study, NOx emission has been estimated by processing the flame image of visible wavelength and its experimental verification has been presented. The experimental study has been performed by using a domestic coal boiler with a capacity of 85000 Kcal / h. The real NOx value has been measured from a flue gas analyzer device. The flame image has been taken by CCD camera from the observation hole on the side of the burner. The data set which is related to instantaneous combustion performance and flame images was recorded simultaneously on the same computer with time stamps once a second. The color flame image has been transformed into a gray scale. Features have been extracted from the gray image of flame. The features are extracted by using the cumulative projection vectors of row and column matrices. ANN regression model has been used as the learning model. The relationship between flame image and NOx emission has been obtained with the accuracy of R = 0.9522. Highly accurate measurement results show that the proposed NOx prediction model can be used in combustion monitor and control systems.

It is an active field of study in studies where the iris center is referenced, such as iris center detection, gaze tracking, driver fatigue detection. In this study, an approach for real-time detection of iris centers based on convolutional neural networks is presented. The GI4E dataset was used as the dataset for the proposed approach. Experimental results estimated the test data of the proposed convolutional neural network model with an accuracy of 97.2% based on the 0.025 error corresponding to the closest position to the iris center according to the maximum normalized error criteria. The study was also tested in real time with a webcam built into the computer. While the test accuracy is satisfactory, real-time speed performance needs to be improved.

The boron element forms more than 600 compounds with different element roots and shows very different properties. Boron compounds with these different properties deserve to be the most crucial strategic feature in the world as they meet the demands above the targeted standards in industries such as energy, structure, chemistry, weapons, and space. Today, the industries of developed countries have begun to take advantage of these energy sources due to the reduction of fossil energy resources, the inability of the industry to store enough electricity for an entire facility, and the limitations imposed on environmental policies. Developing countries continue to use fossil resources, but health and environmental costs are increasing. Whether they are developed or developing countries, they have attached importance to the research of energy systems that can replace fossil energy systems, which are environmentally friendly, sustainable, and high-performance. Boron has an essential role in the energy field for the isolation, high energy value retention, fuel and ion batteries, solar panels, and high-temperature transistors. In this study, the desired properties of boron compounds in energy studies were investigated by considering the positive effects of boron on the energy demand.

Facial recognition is used efficiently in human-computer interactions, passports, driver’s licence, border controls, video surveillance and criminal identification, and is an important biometric’s security option in many device-related security requirements. In this paper, we use Eigenface recognition based on the Principal Component Analysis (PCA) to develop the project. PCA aims to reduce the size of large image matrices and is used for feature extraction. Then, we use the euclidean distance method for classification. The dataset used in this project was obtained by AT&T Laboratories at Cambridge University [1]. The training dataset contains grayscale facial images of 40 people; each person has 10 different facial images taken from different angles and emotions. This study aims to give researchers a hunch before they start to develop image recognition using deep learning methods. It also shows that face recognition can be done without deep learning.

Solving optimization problems is still a big challenge in the area of optimization algorithms. Many proposed algorithms in the literature don’t consider the relations between the variables of the nature of the problem. However, a recently published algorithm, called “Bayesian Multiploid Genetic Algorithm” exploits the relations between the variables and then solves the given problem. It also uses more than one genotype unlike the simple Genetic Algorithm (GA) and it acts like an implicit memory in order to remember the old but good solutions. In this work, the well-known Multidimensional Knapsack Problem (MKP) is solved by the Bayesian Multiploid Genetic Algorithm. And the results show that exploiting relations between the variables gets a huge advantage in solving the given problem.