Vehicle tracking and locking system based on gsm and gps

Data clustering is an important activity in the field of data analytics. It can be described as unsupervised learning for grouping the similar objects into clusters. The similarity between objects is computed through distance measure. Further, clustering has proven its significance for solving wide range of real-world optimization problems. This work presents water wave optimization (WWO) based metaheuristic algorithm for clustering task. It is seen that WWO algorithm is an effective algorithm for solving constrained and unconstrained optimization problems. But, sometimes WWO cannot obtain promising solution for complex optimization problems due to absence of global best information component and converged on premature solution. To address the absentia of global best information and premature convergence, some improvements are inculcated in WWO algorithm to make it more promising and efficient. These improvements are described in terms of modified search mechanism and decay operator. The absentia of global best information component is handled through updated search mechanism. While, the premature convergence is addressed through a decay operator. The performance of WWO algorithm is evaluated using thirteen benchmark clustering datasets using accuracy and F-score parameters. The simulation results are compared with several state of art existing clustering algorithms and it is observed proposed WWO clustering algorithm achieves a higher accuracy and F-score rates with most of clustering datasets as compared to existing clustering algorithms. It is also showed that the proposed WWO algorithm improves the accuracy and F-score rates an average of 4% and 7% respectively as compared to existing clustering algorithm. Further, statistical test is also conducted to validate the existence of proposed WWO algorithm and statistical results confirm the existence of WWO algorithm in clustering field.

A new metaheuristic algorithm based on water wave optimization for data clustering

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt properly, the affected person’s survival rate usually does no longer exceed half a year. This can rapidly cause dying. For this reason, an automatic system is desirable for locating brain tumors at the early stage. In general, when compared to computed tomography (CT), magnetic resonance image (MRI) scans are used for detecting the diagnosis of cancerous and noncancerous tumors. However, while MRI scans acquisition, there is a chance of appearing noise such as speckle noise, salt & pepper noise and Gaussian noise. It may degrade classification performance. Hence, a new noise removal algorithm is proposed, namely the modified iterative grouping median filter. Further, Maximum likelihood estimation-based kernel principal component analysis is proposed for feature extraction. A deep learning-based VGG16 architecture has been utilized for segmentation purposes. Experimental results have shown that the proposed algorithm outperforms the well-known techniques in terms of both qualitative and quantitative evaluation.

Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches

Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the Flow Direction Algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original Flow Direction Algorithm by the arithmetic operators that have been used in the Arithmetic Optimization Algorithm (AOA). The main aim of the proposed FDAOA is to avoid the recognized weaknesses in the original methods; stuck in the local area, premature convergence, and weak equilibrium between the exploration and exploitation search mechanisms. The proposed method is tested on two sets of various problems to validate its performance. In the first set, twenty-three benchmark functions are used, which belong to three categories; seven unimodal functions, six multimodal functions, and ten fixed dimension functions. In the second set, eight common data clustering problems are used to prove the ability of the proposed FDAOA to deal with real-world optimization problems. The results of the proposed method are compared with other well-established methods, and the proposed FDAOA achieved promising output compared to the other methods on various tested problems. The proposed method got the optimal clustering solutions almost in all the tested data clustering problems with clear significant improvements against the other comparative methods. 

Enhanced Flow Direction Arithmetic Optimization Algorithm for mathematical optimization problems with applications of data clustering

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

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A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering algorithm with the objective of increased performance. Ant lion optimization (ALO) technique is the swarm based intelligence that exhibits the hunting mechanism of the ant lions in the natural environment. Ant colony optimization (ACO) algorithm is a swarm based intelligence technique which inherits the behaviour of natural ant. In this paper new hybrid ACO–ALO algorithm was proposed to solve the data clustering problem. Additionally Cauchy’s mutation operator is added with this proposed algorithm to avoid the local minima trapping problem. The main objective is to reduce the intra cluster distance in clustering problem. From the experimental analysis, it evidences the proposed ACO–ALO algorithm outperforms the traditional algorithms of data clustering.

Hybrid metaheuristic algorithm for improving the efficiency of data clustering

The prevailing mechanisms for extracting useful information might offer enhanced results in extraction of useful data for creating classification policies. The goal is to administer the disputes prevailing within the categorization for supervised data. Moreover several schemes conceal the individuality of the schemes employed which attempts to conceal the location of information which might become a serious issue during conserving privacy of the data stored. The aim is to address the disputes by making use of a graph and hypothetical based scheme termed as k-segmentation of graphs which delivers the creation of difficult choice based tree classification organized into a priority based hierarchy. The analysis depicts that the designed scheme offers accuracy and effectiveness.

Upkeeping secrecy in information extraction using ‘k’ division graph based postulates

The quality of Magnetic Resonance Images(MRI) are degraded by the various types of noises. In this paper, a Hybrid Multi-resolution filter for denoising the MRI images degraded by the Salt and Pepper noise is proposed and the wavelet transform is used to improve the resolution of the denoised image.. The Hybrid filter consist of three value weighted filter and similarity based filter. In three value weighted filter, a variable local window is applied to find the noisy pixels. By using the noise free pixels in that window, the noisy pixels are reconstructed using three value method. In similarity based filter, a variable local window is applied to reconstruct the noisy pixels. In that window, based on the similarity between the noisy pixel sequence and noise free pixels sequence are used to reconstruct the noisy pixel. At last wavelet transform is used to increase the resolution of the reconstructed image. The experimental results shows that the proposed filter denoises the image and improves the resolution when compared to the existing methods and produces the efficiency of about 98%.

Hybrid Filtering Approach for Retrieval of MRI Image

The quality of digital images are degraded by the noises. In this paper, a combined filtering approach is proposed to remove the salt & pepper noise. It consist of three value weighted filter and traditional median filter. In three value weighted filter, a variable local window is applied to find the noisy pixels. By using the noise free pixels in that window, the noisy pixels are reconstructed using three value method. In median filter, a variable local window is applied to reconstruct the noisy pixels. In that window the central noisy pixel is reconstructed by the median value of the pixels in that window. At last wavelet transform is used to increase the resolution of the reconstructed image. The experimental results shows that the proposed filter denoises the image and improves the resolution when compared to the existing methods.

A Combined Filtering Approach for Image Denoising

Wireless Sensor Networks are utilized as a part of wellbeing checking, Natural debacles like surge, tremor, thunder storm and so on. Sensor Nodes in WSN made up by little battery control, stockpiling and so on., the fundamental undertaking of sensor organize is handling and solid detected information and exchanging detected information to a base station or sink hub through remote correspondence [1]. This proceeds with control supplies required for base station and sensor hubs has constrained memory and battery reinforcement for viable correspondence is detached. The vitality preservation is a noteworthy test in WSN. In this paper, proposed different procedures for vitality preservation for powerful correspondence [2].

V. P. Arunachalam

Papers

Hybrid metaheuristic algorithm for improving the efficiency of data clustering

Upkeeping secrecy in information extraction using ‘k’ division graph based postulates

Hybrid Filtering Approach for Retrieval of MRI Image

A Combined Filtering Approach for Image Denoising

Energy Efficient Structure Free and Location Based Routing Protocol in WSN