Algorithm

What is an algorithm? According to the textbook, an algorithm is a set of specific sequential steps that describe exactly what the computer program must do to complete the required work. Figure 10.4, on page 426, shows the stages that each programming project follows. Step two is where the algorithm is, which is “making the plan.” The example the book uses is the pharmaceutical companies designing new drugs. When they design new drugs, they have to use complex computer programs that model molecules

2) k-means Algorithm k-means is an unsupervised clustering algorithm which is used to classify input image to k clusters based on the nearest mean. The modified algorithm for k-means derived from [11] is explained as follows 1. Start. 2. Read the input image A. 3. Resize image A to a fixed size of 256× 256. 4. Divide the image into two 2×2 non overlapping blocks. 5. Represent each block in the form of a training vector space X. Each block is converted to the training vector Xi= (xi1, xi2,

''' @summary: To remove keywords that are similar. This is to bring more diversity to the top ranked keywords. We use an algorithm called K-means for grouping. This algorithm is implemented and have not used third party library other than numpy (for computation) and gensim (loading of data set) Each word in the keyword list is converted to a vector using word2vec. We use GoogleNews pre-trained dataset to get vector of word. Then, we divide the n keywords into k clusters. Initially, we randomly choose

sensor node, which is a blend of various parameters including: residual energy, number of neighbors and transmission control. Basically CFL clustering algorithm is designed for localization in WSNs. It is unable to work when the distribution of sensor nodes are not good. 3.2.4 FoVs: Overlapped Field of View Authors proposed a clustering algorithm for wireless sight and sound sensor networks in light of covered Field of View (FoV) areas. The fundamental commitment of this calculation is finding the

1. INTRODUCTION Gossip-based algorithm plays a major part for distributing simple and efficient information in large networks. One of the examples of gossip-based algorithm is rumor –spreading model. It is also called as rumor mongering. It is introduced by Daley & Kendall (D K model) in the context of duplicated databases. The rumor spreading algorithm is an example of epidemic process. It is mainly used to examine in the view of mathematics. The algorithm follows synchronous rounds. The main aim

\paragraph{Spatiotemporal Swarms Mining Algorithms} Algorithm \ref{algo:spatiotemporal} shows the process of mining short time and long time spatiotemporal swarms. Initially, all swarms are empty indicated by Lines 3-5. Then, the trajectory data is preprocessed to remove inconsistent and inaccurate data at Line 6 and build time to locations matrix at Line 7. After that, infrequent pruning, row and column pruning methods have been applied upon matrix data at Lines 8-10 and mines locations based all

In 2015, K. Lenin et. al. [44] in their study “Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem” expressed that to reduce the real power loss, modifications were required in local and global leader phase and a Modified Spider Monkey Algorithm (MMO) was introduced. Paper also upheld that MMO is more favorable for dealing with non-linear constraints. The algorithm was examined on the IEEE 30-bus system to minimize the active power loss. H. Sharma, et al.

3 Greedy Algorithm Greedy technique is an algorithm design policy, built on configurations such as different choices, values to find objective .Greedy algorithms produce good solutions on mathematical problems. The main aim is to find some configurations that are either maximized or minimized. Greedy Algorithms provide a solution for optimization problems that has certain sequence of steps, with a set of choices for each step. Another solution for Greedy algorithm is dynamic programming . It is

From the tree SP we presented in the algorithm that we have obtained via Local Search Algorithm for STP, we have generated the matrix of cost. This is done by assigning a cost to all the edges of tree SP and by assigning a cost on “n” no. of nodes to all the other edges in graph. This assignment of cost helps in recognizing the cost of the longest possible path between a pair of nodes in any spanning tree is n−1 (i.e. it passes n−1 edges) while the cost of the shortest path between any pair of nodes

3.6. Algorithm of sequential gradient search Step 1: Set specifications of the inductor Step 2: Set the values of Bm ,  and no. of core steps. Step 3: 0.45≤ K ≤ 0.6 and 0.3 ≤ Rw ≤ 0.4 Step 4: i = 0 to 30 do: K  0.3  i / 100 Step 5: Calculate cost. Step 6: If cost shows initially low and after that high, (concavity fails for K ) go to step 28 Step 7: i = 0 to 20 do: Rw  2  i /10 Step 8: Go to sub-routine, calculate the cost. Step 9: If cost does not show low value and then high (concavity