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Information theory provides suitable tools for solving practical problems, particularly multi-criteria decision-making (MCDM) problems under neutrosophic environments. Generally, a wide range of MCDM solution methods are constructed based on distance measures category. The defined distance measures for continuous neutrosophic numbers, especially its trapezoidal type, is very limited rather than discrete type. The main goals of this study is to come up with a way to add two new types of weighted distance measures based on meaningful surfaces: Euclidean and Hamming. To define these measures, all three components of the neutrosophic trapezoidal fuzzy number (truth, indeterminacy, and falsity membership functions) have been used simultaneously. The proof of some theorems and properties for the weighted distance measures demonstrates their validity. The CODAS algorithm is known as one of the distance-based methods for solving MCDM problems. The following represents the CODAS algorithm based on two novel distance measures. In addition, an explanatory example from the research literature is given to check the performance of the proposed hybrid algorithm. The results of this study indicate that the algorithm based on the proposed measures obtains a reasonable and appropriate ranking order between the options. Furthermore, the sensitivity parameter analysis and comparative analysis show the flexibility and accuracy of the suggested measures in the combined algorithm. The acceptable efficiency of proposed distance measures formed on the surfaces can shed light on research related to distance measures in the methodology and implicated aspects. [ABSTRACT FROM AUTHOR]

The Randić matrix R = [rij] of a graph G = (V;E) was defined as...j if vertices vi and vj are adjacent and rij = 0 otherwise, where di is the degree of the vertex vi 2 V. In this paper, we define the Randić matrix of a uniform hypergraph and study some its spectral properties. We also define the Randić energy of a uniform hypergraph and determine some upper and lower bound for it. [ABSTRACT FROM AUTHOR]

Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impacts. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep-learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image–mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder–decoder model architecture that uses both synthesized image–mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7% on an internal test dataset and a Dice score of 64.8% on an external test set composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture. [ABSTRACT FROM AUTHOR]

Data mining is one of the stages of acquiring knowledge in a database to collect useful information. Data mining is a new field that has various applications and it is known as one of the top ten sciences affecting technology. Data mining analyzes databases and massive data sets in order to discover and extract knowledge, and machine (and semi-machine) mines. Such studies and explorations can actually be considered the extension and continuation of the ancient and ubiquitous knowledge of statistics. The major difference is the scale, breadth and variety of fields and applications, as well as the dimensions and sizes of today's data, which require machine learning, modeling, and training methods. In the 1960s, statisticians used the term "Data Fishing" or "Data Dredging" to discover any relationship in a very large volume of data without considering any assumptions. After thirty years and with the accumulation of data in databases, the term "Data Mining" became more popular around 1990. The purpose of this research is to predict brain and nerve diseases using data mining algorithms. The purpose of this research is to help medical professionals to predict disease. In this research, after data preparation, disease prediction has been attempted using large matrix methods and data mining techniques. By examining the new vector, we can find out which of the diseases in the matrix will be closer to this new disease with new symptoms using the rows of the matrix. The conducted research is one of descriptive-analytical and applied studies. In this research, we used different meters such as Jacquard distance, cosine similarity L1 -norm and cosine similarity L2 -norm implemented a program using Python software to predict brain and neurological diseases. The algorithm implemented by Python software, the doctor enters the symptoms of the patient and the output of the program shows three diseases close to the input symptoms for each meter, and finally all the meters are compared and the meter that has a weaker result is determined each time it is run. The advantages of each of these meters are explained below. [ABSTRACT FROM AUTHOR]

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Assume we have a set of k colors and to each vertex of a graph G we assign an arbitray of these colors. If we require that each vertex to set is assigned has in its closed neighborhood all k colors, then this is called the generalized k-rainbow dominating function of a graph G. The corresponding γgkr, which is the minimum sum of numbers of assigned colors over all vertices of G, is called the gk-rainbow domination number of G. In this paper, we present a linear algorithms for determining a minimum generalized 2-rainbow dominating set of a tree and on GP(n, 2). [ABSTRACT FROM AUTHOR]

In this paper we have developed a new model by altering Liu and Peng's approach [20] toward ranking method using CSW. In fact, we have adopted a new criterion which is stronger in terms of maximizing efficiencies. After showing advantages of our model theoretically and illustrating it geometrically, two examples demonstrated how the proposed method is practically more capable. [ABSTRACT FROM AUTHOR]

This paper considers minimum cost flow problem in dynamic networks with uncertain costs. First, we present a short introduction of dynamic minimum cost flow. Then, we survey discrete and continuous dynamic minimum cost flow problems, their properties and relationships between them. After that, the minimum cost flow problem in discrete dynamic network with uncertainty in the cost vector is considered such that the arc cost can be changed within an interval. Finally, we propose an algorithm to find the optimal solution of the proposed model. [ABSTRACT FROM AUTHOR]