Even with infinite time, there are some computing issues that algorithms cannot resolve. NP-complete problems are those whose solution is unknown. It's intriguing to note that if one NP-complete question can be resolved in polynomial time, subsequently, all others can be resolved. In this study, we will define a sparse graph, discuss several complexity classes, independent sets, and demonstrate that sparse graphs are NP-complete. What Is A Sparse Graph? A sparse graph is one with a limited number of edges. The total number of edges in this situation is significantly fewer than there could be or the highest possible number ... Read More

Graph coloring is a subset of graph labelling in graph theory. The use of colors stems from coloring the nations on a map, wherein each face is colored. There are several real-world applications for graph coloring, as well as issues of theory. Apart from the traditional forms of problems, other constraints can be imposed on the graph, the manner a color is given, or even the color itself. It has even gained widespread appeal in the shape of the renowned numerical puzzle Sudoku. Graph coloring remains an active area of study. What Is Vertex Coloring? The assignment of colors or ... Read More

Pathfinding algorithms are based on graph search techniques that study paths between nodes, starting with one node and progressing via connections until the objective is attained. In this post, we'll talk about weighted graphs and how to calculate the monotonic shortest route between source and end node in a directed weighted graph. What Are Weighted Graphs? A weighted graph combines a graph with a weight function. That is, it assigns an integer weight to every edge. There are multiple uses for edge weights for a graph − Network Connection Latency  Road Network Distances The Strength of a social network interaction ... Read More

Algorithms concerning graph search algorithms traverse a graph in pursuit of broad discovery or targeted search. These algorithms cut pathways across the network, but no one expects those paths to be computationally optimum. Pathfinding algorithms are assembled on graph search techniques as well as these investigate pathways amongst vertices, beginning at a specific node and going via connections till the goal is visited. What Are Graphs? Graphs are data structures that reflect "connections" among sets of components. These items are known as nodes. Edges are the connections between nodes. Shortest Path The Shortest Path algorithms locate the shortest paths or ... Read More

In this article we will learn about Fusion learning and get to know how its working, its advantages and all other parameters. As technology grows we are getting more concerned about privacy in the field of machine learning. Earlier we used to train the data in centralized form which is more vulnerable to privacy so we are shifting towards Federated learning which allows us to train models by collaborating and without sharing the raw data which is a good technique in terms of privacy. Let’s get to know about Federated Learning. Federated Learning This is a decentralized mechanism of machine ... Read More

In this article we will learn about variance and how to calculate the variance of list. You may have encountered this problem of finding the variance particularly in data science. So in this article we will learn how to find the variance. Variance This tells us how the data is spread, it gives us a measure of the degree of a set of points. We can calculate the variance of the list using various methods. Let’s learn about those methods. Method 1: Using the Statistics Module In this method we will use the built-in statistics model python for calculating the ... Read More

In this article we will learn about variable list slicing. As you know List slicing is very useful and powerful feature of Python using which we can cut out some specific portions of any list quickly. Python provides us various techniques and methods for slicing and extracting the elements based on some specific criteria or patterns. Take an example to understand − list number: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 33, 34, 56, 43, 67] We have list of some random numbers and suppose we want to slice some of the portion from the ... Read More

In this article we will learn about various methods using which we can find the uncommon element in the list of lists that means the element which is not present in the other list. When working with lists of lists in Python, it's common to encounter scenarios where you need to find the uncommon elements present in these nested structures. Finding the uncommon elements in lists of lists can be achieved using various methods and techniques. In this article, we will explore 8 examples and methods to help you easily identify the uncommon elements in your lists of lists. Example ... Read More

In this article we will learn about machine learning program which can find the word Analogy from the provided word. Take an example "Apple : fruit :: car :vehicle". In this analogy, "apple" and "car" are the two things being compared. "Fruit" and "vehicle" are the two categories that the things being compared belong to. The analogy is saying that apple is a type of fruit, just as car is a type of vehicle. So the human brain can identify the pattern but training machine to do the same task will be very difficult as we will require very very ... Read More

In this article we will learn about histograms and we will see detailed view about histogram and its various types. We will also draw see implementation using python. Histogram Histogram provides us visual representation of data, it is used to shows bar chart for numerical data. We can visualize the different distributions and patterns in the dataset. X-axis in the histogram is used to denote the range of values and the y-axis is used to denote the frequency or count of data points. Applications of Histogram 1. Analysis of Data Distribution We use histogram to analyze the data distribution and ... Read More