Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems. Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimization problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism. U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way between its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO). In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper provides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

This paper implements a wireless attack technique by cracking the password on kali Linux OS using Hashcat technique. This study identifies the security weakness, using brute-force attack for online attacking and straight attack for offline attacking. The brute-force attack is also recognized as a detailed search, where it attempts guessing the target password one password at a time until reaching the correct password, which is called a dictionary attack. then using hash algorithms to deal with MD5 hash algorithm and SHA-512 (Linux). In this article, we will learn about the various wireless attacks. These days, wireless networks are everywhere. With users being on the go like never before, having to remain stationary because of having to plug into an Ethernet cable to gain Internet access is not feasible. For this convenience, wireless connections are not as secure as Ethernet connections. In this article, we will explore various methods for manipulating wireless attacks and their techniques including several methods on Linux.

Back propagation neural network are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural network for back propagation of errors, and sigmoid activation function. This neural network to map non-linear threshold gate. The non-linear used to classify binary inputs (x1, x2) and passing it through hidden layer for computing coefficient_errors and gradient_errors (Cerrors, Gerrors), after computing errors by (ei = Output_desired- Output_actual) the weights and thetas (ΔWji = (α)(Xj)(gi), Δϴj = (α)(-1)(gi)) are changing according to errors. Sigmoid activation function is = sig(x)=1/(1+e-x) and Derivation of sigmoid is = dsig(x) = sig(x)(1-sig(x)). The sig(x) and Dsig(x) is between 1 to 0.

Recent days, the concept of data mining and the need for it, its objectives and its uses in various fields, explain its procedures and tools, the type of data that is mined, and the structural structure of that data while simplifying the concept of databases, relational databases and the query language. Explain the benefits and uses of mining or mining data stored in specialized databases in various vital areas of society. Also, it is the process of analyzing data from different perspectives and discovering imbalances, patterns and correlations in data sets that are insightful and useful for predicting results that help you make a good decision. Let's bring back our mining example, when you plan to prospect for gold or any valuable minerals you first have to determine where you think the gold is to start digging. In the process of data mining we have the same concept. To mine data, you must first collect data from various sources, prepare it, and store it in one place, as nothing from data mining is related to the process of searching for the data itself. Currently, the company is storing data in what is called a Datawarehouse which we will talk about in a later stage in detail.